Intro to the Workshop and Logistics

• Cristina Rea (Massachusetts Institute of Technology)
• Matteo Barbarino (International Atomic Energy Agency)

Room: Conference Room 1 (CR1), C Building, 2nd floor

Intro to Physics/Engineering Track

• Ryan McClarren (University of Notre Dame)

Room: Conference Room 1 (CR1), C Building, 2nd floor Ryan McClarren (ND, USA), Hideo Nagatomo (Osaka U., Japan), Marcin Jakubowski (IPP, Germany)

Machine Learning Enabled Quantitative Prediction of the First-Ever Igniting Inertial Confinement Fusion Experiment (Physics/Engineering)

• Bogdan Kustowski (Lawrence Livermore National Laboratory)

Room: Conference Room 1 (CR1), C Building, 2nd floor On December 5, 2022, scientists at the Lawrence Livermore National Laboratory carried out the first-ever Inertial Confinement Fusion experiment that met all criteria for ignition. This 2.05-megajoule laser shot at the National Ignition Facility compressed a millimeter-size capsule containing hydrogen fuel, leading to fusion reactions, and generating 3.15 megajoules of energy, a gain of 1.5. The outcome of this historic experiment did not come as a big surprise; a pre-shot analysis predicted a much higher chance of ignition for this new design relative to previous designs. In this talk, we describe a new method developed to make this prediction. The method combines a very sparse dataset of previous experiments with a large ensemble of simulations, Bayesian inference to incorporate uncertainties, machine learning to make the technique computationally feasible, and is built on several years of experience with the analysis of inertial confinement experiments. During the months that followed the ignition shot, we have adapted and applied this method to predict subsequent experiments, which begin to form a validation set for our predictive model. The predictive modeling is expected to play an increasing role in evaluating new designs and facility upgrades, or determining driver requirements for inertial fusion energy needs. LLNL-ABS- 850875. Prepared by LLNL under Contract DE- AC52-07NA27344.

Data-driven model for divertor plasma (Physics/Engineering)

• Ben Zhu (Lawrence Livermore National Laboratory)

Room: Conference Room 1 (CR1), C Building, 2nd floor Magnetically confining a high temperature plasma in a toroidal device (e.g., tokamak, stellarator, etc) is arguably the most promising approach for mankind to archive controlled thermonuclear fusion energy. One critical concern of this approach is to find and maintain the proper heat and particle exhaust at the divertor region – a region at where the magnetic topology changes from “closed” to “open” and the high temperature plasma may be directly in contact with the vessel wall. Modeling divertor plasma is not a trivial task due to its multi-physics, multi-scale nature. For instance, the widely used axisymmetric 2D edge transport codes in the community (e.g., SOLPS, UEDGE, etc) often take days or even months to attain converged steady state divertor solutions once sophisticated plasma and neutral dynamics are included. This time-consuming process not only affects the divertor plasma physics research but also impacts the high-fidelity divertor model applications, e.g., in new device design, discharge scenario development and real-time plasma control. Machine learning technique offers an alternative solution to this challenge. A fast yet fairly accurate data-driven surrogate model for divertor plasma prediction is possible by leveraging the latent feature space concept. The idea is to construct and train two neural networks – an autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the desired multi-modal diagnostic measurements, and a forward model using multi-layer perception (MLP) that projects a set of divertor plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this data-driven surrogate model predicts a consistent set of diagnostic measurements based on a few key parameters controlling the divertor plasma. This idea is first tested to predict downstream plasma properties with limited upstream information in one-dimensional flux tube configuration. The resulting surrogate model is at least four orders of magnitude faster than the conventional numerical model and provides fairly accurate divertor plasma predictions, usually within a few percent relative error margin. It has 99% successful rate predicting divertor plasma detachment – a bifurcation phenomenon features a sudden decrease of heat load on the divertor plate. This pilot study successfully demonstrates that the complicated divertor plasma state has a low-dimensional representation in latent space that could be utilized for surrogate modeling. Following the same methodology, we recently extend the work to modeling realistic 2D axisymmetric configuration. Application-specific surrogate models are constructed, trained, and tested. These models appear to be able to fulfill many different tasks (e.g., initial solution prediction for code acceleration, integrated tokamak divertor design, and divertor plasma detachment control), suggesting that machine learning is a powerful tool for divertor plasma physics and fusion energy research.

Fast surrogate models for power and particle exhaust in fusion (AI)

• Sven Wiesen (Forschungszentrum Jülich GmbH)

Room: Conference Room 1 (CR1), C Building, 2nd floor Accurate simulations of the scrape-off layer plasma in a tokamak employing state-of-the-art numerical models (e.g. SOLPS-ITER) require long convergence times. Such physically sophisticated models are required for a detailed design e.g. of the divertor in DEMO or in a fusion power plant (FPP). For design scoping studies or integration of models with the core-plasma, currently used reduced fidelity models lack either accuracy or capabilities. Other applications include control specific needs w.r.t. fast model based predictors for the level of plasma detachment in the divertor. All this warrants the development of surrogate models, which interpolate between existing simulations, to allow for fast and accurate results in the whole parameter space. We created a neural network model by training on a database of reduced fidelity fluid neutral SOLPS-ITER simulations. This database includes a cross-machine size scaling, making the developed model applicable to many devices and scenarios. The neural network model is capable of computing the electron temperature in the whole 2D SOL and divertor domain for different physical regimes in less than a second. The deviations between the neural network model and the original simulations are less than 10 percent for the majority of cases. The model is compared to high-fidelity SOLPS-ITER simulations from the ITER IMAS database. Two approaches are tested to reduce the deviations between the model and the ITER simulations: Scaling of the gas-puff parameters and transfer learning. While both reduce the deviations only transfer learning allows for predicting full 2D plasma profiles on the ITER geometry. Some applications for such a model are showcased.

Leveraging physics-informed AI computing for simulating the transport of fusion plasmas (Physics/Engineering)

• Jaemin Seo (Chung-Ang University)

Room: Conference Room 1 (CR1), C Building, 2nd floor For decades, plasma transport simulations in tokamaks have employed the finite difference method (FDM) to solve the transport equations, a coupled set of time-dependent partial differential equations. In this conventional approach, a significant number of time steps, typically over $O(10^5)$, are needed for a single discharge to prevent numerical instabilities induced by stiff transport coefficients. This results in significant computing time as costly transport models are repeatedly called in a serial manner, proportional to the number of time steps. Additionally, the unidirectional calculation inherent in FDM presents challenges for predicting regions prior to the initial condition or for applying additional temporal constraints. In this work, we introduce a novel solution scheme for plasma transport simulation, using physics-informed neural networks (PINNs). Instead of adopting the traditional chronological computations of FDM, this new technique iteratively refines a function mapping spatiotemporal coordinates to plasma states, gradually minimizing errors in transport equations. The required number of iterative updates in PINNs is several orders of magnitude less than the chronological iterations in traditional FDM, and this approach is free from numerical instabilities arising from discretization on finite grids. Furthermore, the flexibility of PINNs enables more versatile "semi-predictive" simulations, permitting the application of arbitrary spatiotemporal constraints, such as sparse and finite conditions or intermediate temporal constraints, which more preserve the diagnostic fidelities. In this presentation, we discuss the features and potentials of our newly proposed PINN-based tokamak transport solver.

Avoiding plasma instabilities with artificial intelligence (AI)

• Egemen Kolemen (Princeton University)

Room: Conference Room 1 (CR1), C Building, 2nd floor For stable and efficient fusion energy production using a tokamak reactor, maintaining high-pressure hydrogenic plasma without plasma disruption is essential. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to maneuver high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle avoidance problem for which artificial intelligence (AI) based on reinforcement learning has recently shown remarkable performance. However, the obstacles here, the tearing instability, are difficult to forecast and highly prone to terminating plasma operations. In our recent work, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators. This dynamic model not only predicts the possible onset of tearing instability during tokamak operation but can also be used as a training environment for AI that controls actuators to avoid instabilities. In this work, we demonstrate AI control based on reinforcement learning to lower the possibility of disruptive tearing instabilities in DIII-D, the largest magnetic fusion facility in the US. The con- troller maintained the tearing likelihood under a given threshold, under relatively unfavorable conditions of low safety factor and low torque.

AI-driven tokamak control in HL-2A/2M (AI)

• Shuo Wang (Southwestern Institute of Physcis)

Room: Conference Room 1 (CR1), C Building, 2nd floor Machine learning and artificial intelligence (ML/AI) methods have been applied to fusion energy research for over 2 decades, including the areas of disruption prediction, particle distribution and loss prediction, plasma equilibrium reconstruction and so on. The success in achieving magnetic control of the TCV tokamak with deep learning methods has demonstrated great opportunities for intelligent control of fusion power plants by ML/AI. There are currently huge needs of AI/ML for accelerated progress toward realization of the fusion energy. This work reports progresses on AI-driven tokamak control in HL-2A/2M based on both the experimental and simulation data, covering the areas of disruption prediction, the magneto-hydrodynamic (MHD) modes recognition, and predicting the operation beta limit. The electromagnetic and thermal energy loads on the first wall and other plasma facing components during tokamak disruptions are often sufficiently high to cause huge damages to the device. Disruptions are hardly avoidable in tokamaks, and are often difficult to predict. AI/ML provides a potential way to address this issue. A machine learning model has been trained to predict disruptions in HL-2A. Further efforts have been made to improve accuracy and interpretability of the model. Both the off-line and real-time tests achieved good performance. The tokamak plasma disruption can be due to many factors, with the macroscopic MHD instability being one of them. It is known that the pressure-driven external kink (XK) mode sets a ‘hard’ limit to the tokamak operation. As the plasma pressure exceeds the so-called Troyon $\beta_N$ limit, major disruptions can occur. The operational $\beta_N$ limit can be calculated with first-principle MHD codes but is always time-consuming. To meet the requirements of the XK mode control in real time in experiments, artificial neural networks (NNs) have been trained to predict the no-wall and ideal-wall $\beta_N$ limits based on the numerical database generated for HL-2M. The NN-prediction is found to reach 95% accuracy compared to the numerical results directly computed by the MARS-F MHD stability code. The edge localized mode (ELM) is another type of instability, that may not cause plasma disruption but can produce a large amount of heat and particle fluxes to the divertor target, which in turn potentially cause material erosion in future reactor-scale devices such as ITER. Therefore, large ELMs need to be identified and controlled, with the latter achieved by 3D magnetic coils, impurity injection or other means. We report an example of ELM recognition and mitigation experiment by the AI control system. An advanced AI model for the ELM identification via L-H transition will also be reported. Similar methods have been employed to identify the long-live mode, the tearing mode, the fish bone mode and the sawtooth in HL-2A. As the last part of the report, we show some of the AI modeling effort which is still in progress, i.e., the device-independent disruption prediction for HL-2M with the data support from HL-2A and J-TEXT experiments, and the intelligent strategy during operations.

Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment (AI)

• Abhilash Mathews

Room: Conference Room 1 (CR1), C Building, 2nd floor Edge plasma turbulence is critical to the performance and operation of magnetic confinement fusion devices. Drift-reduced Braginskii two-fluid theory has for decades been widely applied to model boundary plasmas with varying success. Towards better understanding edge turbulence in both theory and experiment, a custom-built physics-informed deep learning framework constrained by partial differential equations is developed to accurately learn turbulent fields consistent with the two-fluid theory from partial observations of electron pressure. This calculation is not otherwise possible using conventional equilibrium models. With this technique, the first direct quantitative comparisons of turbulent field fluctuations between electrostatic two-fluid theory and electromagnetic gyrokinetic modelling are demonstrated with good overall agreement found in magnetized helical plasmas at low normalized pressure. To translate these computational techniques to experimental fusion plasmas, comprehensive 2-dimensional diagnostics operating on turbulent time scales are necessary. For this purpose, a novel method to translate brightness measurements of HeI line radiation into local plasma fluctuations is demonstrated via a newly created deep learning framework that integrates neutral transport physics and collisional radiative theory for the $3^3 D - 2^3 P$ transition in atomic helium. Using fast camera data on the Alcator C-Mod tokamak, this thesis presents the first 2-dimensional time-dependent experimental measurements of the turbulent electron density, electron temperature, and neutral density in a fusion plasma using a single spectral line. With this experimentally inferred data, initial estimates of the 2-dimensional turbulent electric field consistent with drift-reduced Braginskii theory under the framework of an axisymmetric fusion plasma with purely toroidal field are calculated. The inclusion of atomic helium effects on particle and energy sources are found to strengthen correlations between the electric field and electron pressure while broadening turbulent field fluctuation amplitudes which impact ${\bf E \times B}$ flows and shearing rates.

Intro to AI Track

• Randy Churchill (Princeton Plasma Physics Laboratory)

Room: Conference Room 1 (CR1), C Building, 2nd floor Michael Churchill (PPPL, USA), Cristina Rea (MIT-PSFC, USA), Zongyu Yang (SWIP, China)

Hybrid Bayesian Optimisation / Evolution Strategy applied to the design of uni-axially driven ICF targets (AI)

• Thomas Edwards (First Light Fusion Ltd.)

Room: Conference Room 1 (CR1), C Building, 2nd floor Inertial confinement fusion (ICF) relies on the implosion of precision engineered capsules containing DT fuel. The implosion is initiated by a driver, usually a laser, and the target may feature one or more outer shells to enable driver coupling. First Light Fusion’s (FLF) novel approach separates the design of the target into a fuel capsule and a shock amplifier, which is uni-axially driven by a hyper-velocity projectile. The target design process seeks to maximise the neutron yield of the implosion. This is typically accomplished through optimising the design using radiation-hydrodynamics (rad-hydro) simulations and comparison with experiments. The optimisation process can require thousands of high-fidelity simulations, taking a large amount of compute resource. For these reasons, numerical optimisation techniques are of key interest to the ICF community and several algorithms have previously been proposed. Bayesian Optimisation (BO) is a promising approach to these problems, as it excels at finding the global optimum of expensive black box objectives. Within the Bayesian optimisation loop, a machine learned model (or emulator) is constructed from an initial set of simulation runs and optimised in place of the actual function. This suggests a best set of design parameters; and evaluations at those parameters (made by running more rad-hydro simulations) are used to update the emulator. The process is repeated to rapidly locate the optimum. While extremely powerful, Bayesian Optimisation has extensive configuration options and effective use requires detailed knowledge of the problem being optimised. In this work, we describe a comprehensive Bayesian Optimisation (BO) capability tailored for use in ICF target design problems. The BO routines are implemented using BoTorch, an open-source Python framework. Gaussian process (GP) models are used as emulators, which fit a normally distributed family of response surfaces to the simulation samples, the initial set of such being generated by an optimised Latin Hypercube space-filling design. GP models can struggle with noisy and discontinuous response surfaces common in ICF simulation. This is solved by using a data-learned transform on the objective. Asynchronous batch execution (using the “Kriging Believer” heuristic for conditioning the GP on pending inputs) is implemented, allowing full use of HPC compute resources. Black box constraints, commonly found in ICF problems, are handled through multiple output tasks (the objective and the constrained outputs) learned simultaneously and used to constrain the space of input parameters that are suggested by the BO algorithm. The approach is benchmarked against a robust and commonly used optimisation algorithm, CMA-ES (Covariance Matrix Adaptation Evolution Strategy) and the EGO (Efficient Global Optimisation) Bayesian Optimisation algorithm available in the DAKOTA toolkit (developed by Sandia National Laboratory). Our approach outperforms both CMA-ES and EGO for optimising the performance of our amplifier design (a 9D problem) and on other synthetic benchmarks. Finally, as BO is well suited for locating global optima but can be slow to refine to the exact location, we develop an algorithm that combines Bayesian Optimisation with CMA-ES, called BOCMA, which outperforms both CMA-ES and BO in terms of the number of iterations required.

Bayesian optimization for efficient parameter space coverage with computationally demanding simulations in fusion energy applications (AI)

• Aaro Järvinen (VTT)

Room: Conference Room 1 (CR1), C Building, 2nd floor Magnetic confinement fusion research is characterized by computationally demanding physics models with a selection of uncertain, phenomenological input parameters. Rigorous usage of such models for predictive or interpretative applications requires a thorough inverse uncertainty quantification (UQ) for these input parameters [1]. Bayesian inference (BI) algorithms provide a principled approach to quantify the uncertainty, as a probability distribution, for the state of the investigated system or hypothesis validity, given the available information [2]. When operating with computationally costly models, data-efficiency is key to maximizing the information gain in establishing this probability distribution. Such efficiency can be achieved by combining “Bayesian optimization” (BO) with the overall BI task. BO is a powerful framework for data-efficient global optimization of costly, non-convex functions, without access to first- or second-order derivatives [3]. On the one hand, BO uses BI to build a statistical approximation in the space of functions that represents the costly model, leveraging the Bayesian quantification of uncertainty over functions to efficiently refine the approximation where needed. On the other hand, the overall BI task is focused on establishing posterior probability distributions over the uncertain state of the investigated system or hypothesis. Crucially, the overall BI task can be conducted with or without BO, but the application of BO renders the BI task orders of magnitude more data-efficient. For many practical applications in fusion energy research, batch BO is needed [4]. The standard BO algorithms conduct sequential search, where a computationally relatively light to evaluate acquisition function is optimized to recommend the highest utility sample to collect with the computationally demanding model. However, if evaluation of the full model takes longer than a few hours, which is quite typical in fusion research applications, the overall optimization time with a sequential approach can easily become unacceptably long. Batch BO can be used to collect several samples in parallel to each other, accelerating the overall optimization task to the throughput levels required for practical applications in fusion energy research. This work discusses BI and BO work performed within or in close connection to the EUROfusion Advanced Computing Hub, hosted by the University of Helsinki (ACH 5). The example applications presented in this work encompass runaway electron simulations [5, 6], scrape-off layer (SOL) plasma simulations, integrated plasma scenario simulations, and tokamak experiment design through BO algorithms [7]. [1] WU, X., et al., Nucl. Eng. Des. 335 (2018) 339-355. [2] VON TOUSSAINT, U., Rev. Mod. Phys. 83 (2011) 943. [3] FRAZIER, P.I., A Tutorial on Bayesian Optimization, (2018) arXiv:1807.02811. [4] HUNT, N., et al., Batch Bayesian Optimization, Thesis: S.M., MIT, 2020. [5] NARDON, E., et al., Modelling of runaway electron dynamics in tokamak disruptions, Proceedings of 29th IAEA Fusion Energy Conference, 16-21 October, 2023, London, United Kingdom. [6] JÄRVINEN, A.E., et al., J. Plasma Phys. 88 (2022) 905880612. [7] CLARTÉ, G., et al. Proceedings of the 4th International Conference on Data-Driven Plasma Science, 16 – 21, April, 2023, Okinawa, Japan.

Machine learning optimization for laser driven implosions towards high gain fusion (AI)

• Xiaohu Yang (National University of Defense Technology)

Room: Conference Room 1 (CR1), C Building, 2nd floor Laser-driven inertial confinement fusion is an important approach to achieve controllable nuclear fusion. It applies high-power laser pulses or X-rays to ablate the outer surface of a spherical target, leading to a centripetal implosion and an increase in pressure and temperature of the fuel. In order to reach the Lawson criterion and thus realize a self-sustaining burning plasma, we have to compress the fuel to several hundred times of the solid density and rise the target temperature to over 5 keV. Isentropic compression can realize such compression. However, it is not easy to design the optimum target structure and the corresponding laser pulse manually. Firstly, to realize an efficient implosion, the driven laser pulse and target structure are designed using a random walk method for a given laser energy. It can quickly optimize the laser pulse and target structure parameters for an efficient isentropic compression of the target. A correlation matrix can also be constructed to analyze the correlation between the parameters. Secondly, we propose a hybrid optimization method by combining the random walk and the Bayesian methods, to further improve the optimization efficiency. The series of laser pulses and target structures that produces relatively high areal density obtained by the random walk optimization are used as the basic sampling data of the Bayesian optimization. It greatly reduces the desired number of samples for Bayesian optimizations and the Bayesian optimization also makes up for the small step size and low efficiency of the random walk method in the later stage of optimization, and reduces the randomness in the optimization process. The hybrid optimization method greatly improves the optimization efficiency, and has been applied to the experiments of the Double-Cone ignition scheme. We believe that it will play a greater role in the future laser fusion experiments. [1] Z. Li, X. H. Yang, H. Xu, G. B. Zhang, B. Zeng, S. J. Chen, Y. Y. Ma, F. Y. Wu, and J. Zhang, Design of laser pulse shapes and target structures by random optimization for direct-drive inertial confinement fusion. Physics of Plasma 29, 092705 (2022). [2] Z. Li, Z. Q. Zhao, X. H. Yang, G. B. Zhang, Y. Y. Ma, H. Xu, F. Y. Wu, F. Q. Shao, J. Zhang, Hybrid optimization of laser-driven fusion targets and laser profiles. Submitted.

Improving Predictions Under Uncertainty of Material Plasma Device Operations (AI)

• Rick Archibald

Room: Conference Room 1 (CR1), C Building, 2nd floor Understanding the properties of materials when exposed to various plasma temperatures and fluxes is essential to the building and operating of fusion reactors. The Material Plasma Exposure experiment (MPEX) is an instrument currently being developed by the Department of Energy (DOE) for this purpose. MPEX is expected to come online in stages over the next five years. Proto-MPEX, the predecessor to MPEX, operated from 2014 to 2021, and was designed to understand the generation of plasma temperatures and fluxes at orders of magnitude below what will be obtained by MPEX. This work we summarize research developed using stochastic neural network (SNN), a machine learning technique capable of operating under uncertainty to provide a surrogate model for the Proto-MPEX device (Archibald, et.al., 2022). We demonstrate that SNN outperforms Bayesian neural network (BNN), a standard in the field of machine learning with uncertainty. The development of a robust surrogate of the Proto-MPEX will aid in the commissioning and operation of the MPEX device. References Archibald, R., Cianciosa, M. and Lau, C., 2022, December. Improving Predictions Under Uncertainty of Material Plasma Device Operations. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 3402-3407). IEEE

Deep Learning and Machine Learning algorithms for disruption prediction and heat-load monitoring in fusion devices (AI)

• BARBARA Cannas (ENEA-University of Cagliari)

Room: Conference Room 1 (CR1), C Building, 2nd floor Developing reliable control systems for long pulse operation in fusion devices is crucial and challenging for the development of ITER and DEMO. In this context, two of the most critical issues are plasma-facing components (PFCs) protection from high heat loads and disruption  prevention. This talk deals with Machine Learning (ML) tools  developed for machine protection from these two issues, focusing on  state-of-the-art techniques. ML models  for heat load  monitoring and protection from  overloads and for real-time monitoring disruption risk during  plasma evolution will be described. Real-time monitoring of the heat flux (HF) on  PFCs is a key objective for high-performance fusion operation. At W7–X, infrared cameras monitor the PFCs by measuring the surface temperature. Typically, the HF is localized on specific regions of the divertor called strike-lines. Since  high HF can damage the PFCs, a lot of effort is devoted to the estimation of HF on the divertor tiles and to strike-line control . THEODOR (Thermal Energy Onto DivertOR) code computes the HF by numerically solving the heat equation, but the computation time does not allow the real-time application. A new approach based on Physics Informed Neural Networks (PINNs) is proposed for solving the heat equation. PINN are NNs that learn Partial Differential Equations (PDEs) by minimizing the PDE loss in a mesh-free domain. Integrating PI laws into state-of-the-art NN architecture  allows PINNs to real-time  estimate the HF on the divertor tiles. Moreover, a Deep CNN was trained to learn  an inverse model, to determine the  control coils currents (actuators) necessary to achieve a desired  HF distribution (desired state) at W7-X. Control coils were installed for an active control of  thermal  power distribution on the divertor.  The  HF images were obtained by the analysis of thermographic data. Understanding and modelling the relationship between the HF distribution in the strike-lines and the actuators influencing them important step toward  strike-line control. Disruptions  are an  unforeseen loss of plasma confinement inducing thermal loads on  PFC and electromagnetic forces on surrounding structures.  Even if present devices are not extremely affected by  disruptions, the consequences of disruption events for future tokamaks and reactors could be ruinous, due to the higher amounts of stored thermal and magnetic  energies. Disruption causes are not always understood, and first principle models are able to exhaustively  explain only some disruption  dynamics. Their prediction, mitigation and  avoidance are  critical needs for the success of next-step fusion devices. Different ML  models will be presented, including supervised techniques, such as CNNs, and unsupervised techniques, such as Self Organising Map (SOM) or ISOmetric MApping (ISOMAP), applied  both to  disruption prediction in tokamak. Each technique brings its own specific advantages: CNNs allow one to efficiently manage  the spatiotemporal  information from the plasma profiles (temperature, density and radiation), together with other commoly used diagnostic signals. In SOMs and ISOMAP no human intervention is needed during training: in particular, labelling of the samples of disruptive discharges, necessary to supply the model with the information about the presence of disruption precursors in each time instant, is not required.

A deep learning-based model for the cross-scale instability in fusion plasma (AI)

• Hui Li (Dalian University of Technology)

Room: Conference Room 1 (CR1), C Building, 2nd floor Plasmas and plasma-enabled technologies are pervasive in everyday life, but their nonlinear, multiscale behaviors bring challenges for understanding, modeling, and controlling these systems. Accurately revealing the physical mechanism of plasma may provide crucial information for the successful plasma control in real-time tokamak discharge. However, the computation demands a realistic description of system size and timescales remains challenging for many problems. With the emergence and development of artificial intelligence (AI) as well as deep learning (DL) algorithms, these changed, especially in fluid mechanics. In this report, we provide a deep learning-based surrogate model considering the predictions of multi-scale multi-mode instability in real-time conditions. The surrogate models are developed based on tokamak plasma. In this research, the schematic diagram for the construction of models is defined. The surrogate models can be applied in predicting transport properties both with linear and nonlinear phases. With a further development, the models can predict the interaction of multi-scale multi-mode instability. The newly constructed surrogate models show the sufficient ability to reproduce the same simulation results. Furthermore, the newly DL model can accurately obtain the physical characteristics of MHD and micro-instability in tokamak with a computational cost of only a small amount of CPU time. Moreover, the DL-based models may play a vital role in the ‘Intelligent Controlling’ in tokamak device in future.

PFNN：Less data and better performance on disruption prediction via physics-informed deep learning (Physics/Engineering)

• Zongyu Yang

Room: Conference Room 1 (CR1), C Building, 2nd floor Disruption prediction and mitigation is a crucial topic, especially for future large-scale tokamaks, due to disruption’s concomitant harmful effects on the devices. Recent progresses have proved that deep neural network can accurately predict the coming disruptions by learning from history experimental data, which becomes a potential solution for the disruption prediction in future devices [1, 2]. This technique routine has also been proved in HL-2A by offline testing and online experiment [3, 4]. However, a key issue is whether deep learning models can be developed on future devices, which can only tolerate a few disruptions and therefore can’t provide much training data [5]. In this research, a predict-first neural network (PFNN) is developed. Two predict-first tasks are designed to embed physical knowledge into the neural network. Ablation experiments show that the embedded physical information significantly improves the algorithms’ performance, especially when the amount of training data is limited. The first predict-first task is to let the neural network predict the evolution of electronic temperature (Te), electronic density (Ne) and horizontal displacement (Dh) according to the control actuators and the plasma state. A preparatory neural network based on encoder-decoder framework [6] is trained on this task and three empirical equations are hidden in the design of its structure. After the training, the neural network learns the three equations and can accurately predict the evolution of paramters. Then this preparatory network can be used as a feature extractor in the disruption prediction algorithm and use the three equations to promote the performance of disruption prediction. The second predict-first task is to mask part of the experimental data and let the neural network restore them. Another preparatory neural network based on masked auto-encoder framework [7] is trained. It can realistically reconstruct the masked parts according to the unmasked parts and the correlation between different input signals. This preparatory neural network can also be used as a feature extractor in the disruption prediction algorithm and use the correlation between different input signals to promote the performance of disruption prediction. Ablation experiments show that the embedded physical information significantly improves the algorithms’ performance. When the amount of training shots is limited to 1283 shots, the AUC (area under receiver-operator characteristic curve) of PFNN is 5% higher than the ordinary one. In general, PFNN, which is pretrained by predict-first tasks to learn physical information and then trained for disruption prediction in the second step, performs much better on disruption prediction when the amount of training data is limited. It can be a potential solution for future tokamaks’ disruption prediction problem. References [1] Julian Kates-Harbeck et al. Nature 568: 526-531. [2] Jinxiang Zhu et al. Nuclear Fusion 61(11): 114005 [3] Zongyu Yang et al. Nuclear Fusion 60(1) 016017 [4] Zongyu Yang et al. Fusion Engineering and Design 182:113223 [5] P.C. de Vries et al. Fusion Science and Technology 69(2): 471-484. [6] Cho K et al. arXiv preprint arXiv:1406.1078, 2014. [7] Kaiming He et al. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 16000-16009.

Intro to Enabling Infrastructure Track

• Alessandro Pau (Ecole Polytechnique Fédérale de Lausanne (EPFL), Swiss Plasma Center (SPC), CH 1015 Lausanne, Switzerland)

Room: Conference Room 1 (CR1), C Building, 2nd floor Alessandro Pau (EPFL-SPC, Switzerland), Michael Churchill (PPPL, USA), Fuyuan Wu (SJTU, China)

The European High Performance Computing Joint Undertaking - Leading the way in European supercomputing (Enabling Infrastructure)

• Linda GESENHUES (European High-Performance Computing Joint Undertaking)

Room: Conference Room 1 (CR1), C Building, 2nd floor The European High Performance Computing Joint Undertaking (EuroHPC JU) is a joint initiative between the European Union, European countries and private partners to develop a world class supercomputing ecosystem in Europe. It pools resources from the stakeholders and coordinates their efforts to invest in research, innovation and the deployment of a world-leading HPC infrastructure in Europe. The EuroHPC JU seeks to provide computing capacity, improve cooperation in advanced scientific research, boost industrial competitiveness, and ensure European technological and digital autonomy. The EuroHPC infrastructure, which includes some of the largest supercomputers in the world, is available to researchers for open research and development. Moreover, the EuroHPC JU launches open calls for proposals to fund research, innovation and training in HPC.

Physics-Preserving AI-Accelerated Simulations of Turbulent Transport (Physics/Engineering)

• Frank Jenko (Max Planck Institute for Plasma Physics)

Room: Conference Room 1 (CR1), C Building, 2nd floor Predictive and reliable simulations of fusion plasmas provide one important pathway towards accelerating fusion research. A popular approach for efficiently computing the dynamics of turbulent systems for a wide range of applications is the Large Eddy Simulation (LES) technique. Here, the system is simulated with only the largest scales resolved explicitly, while the unresolved scales are accounted for by a Sub-Grid-Scale (SGS) model. In this presentation, we will give this old idea a new twist. Specifically, we will develop an SGS model based on a Neural Network (NN) with Learned Corrections (LC) on the resolved scales to create a hybrid numerical and ML approach. As will be demonstrated, by using a non-propagated field, this approach can be remarkably effective allowing us to cut off virtually the entire inertial range, just retaining the drive range. This is fundamentally different from previous studies, which focused on the much simpler problem of removing diffusion-dominated scales in the dissipation range. However, removing (large) parts of the inertial range while retaining the integrity of the cascade dynamics has been the major challenge facing LES approaches. Simply extending approaches that work within the dissipation range to the inertial range is typically not a viable option. Here, we introduce a model that is able to overcome these difficulties and do so very efficiently. In fact, it is able to produce physically indistinguishable results even when removing (large) parts of the inertial range, while allowing for a relative speedup of about three orders of magnitude.

Exploration of Quantum Computing for Fusion Energy Science Applications (Enabling Infrastructure)

• Ilon Joseph (Lawrence Livermore National Laboratory)

Room: Conference Room 1 (CR1), C Building, 2nd floor Quantum computing promises to deliver large gains in computational power that can potentially have a beneficial impact on a number of Fusion Energy Science (FES) application areas that rely on either intrinsically classical or intrinsically quantum calculations. This work presents an overview of our recent efforts [1] to develop and extend quantum algorithms to perform FES-relevant calculations and perform concrete examples of quantum computations on present-day quantum computing hardware platforms. We have developed quantum algorithms that can: (1) exactly simulate the Liouville equation [2], even for nonlinear non-Hamiltonian, e.g. dissipative, classical dynamics; (2) perform efficient eigenvalue estimation for generalized eigenvalue problems common in plasma physics and MHD theory [3]; (3) efficiently implement nonlinear wave-wave interactions [4]; and (4) efficiently explore chaotic quantum and classical dynamics [5,6]. Simplified versions of these quantum algorithms have been implemented on state-of-the-art cloud-based superconducting architectures such as the IBM-Quantum Experience and Rigetti Quantum Cloud Services platforms to test the fidelity of emerging quantum hardware capabilities. We have also implemented some of these algorithms on the LLNL Quantum Design and Integration Testbed (QuDIT), which has novel capabilities such as the ability to work with more than two energy levels per transmon and the ability to synthesize arbitrary unitary gates (for small qubit numbers) using optimized control pulses. These hardware platforms have been used to simulate a nonlinear three-wave interaction problem [4] and a three-level Grover’s search algorithm. We have also explored the ability of the IBM-Q platform to simulate chaotic dynamics through the quantum sawtooth map [5,6], as well as a number of the building blocks of the quantum variational eigensolver algorithm. The fidelity of the experimental results matches noise models that include decay and dephasing processes and highlights key differences between state-of-the-art approaches to quantum computing hardware platforms. *LLNL-ABS-833451 was prepared by LLNL for U.S. DOE under Contract DE-AC52-07NA27344 and was supported by the U.S. DOE Office of Fusion Energy Sciences “Quantum Leap for Fusion Energy Sciences” project SCW1680. References [1] I. Joseph, Y. Shi, M. D. Porter, A. R. Castelli, V. I. Geyko, F. R. Graziani, S. B. Libby, J. L. DuBois, “Quantum computing for fusion energy science applications,” Phys. Plasmas 30, 010501 (2023). [2] I. Joseph, “Koopman-von Neumann approach to quantum simulation of nonlinear classical dynamics,” arXiv:2003.09980, Phys. Rev. Research 2, 043102 (2020). [3] J. B. Parker, I. Joseph. “Quantum phase estimation for a class of generalized eigenvalue problems,” arXiv:2002.08497, Phys. Rev. A 102, 022422 (2020). [4] Y. Shi, A. R. Castelli, X. Wu, I. Joseph, V. Geyko, F. R. Graziani, S. B. Libby, J. B. Parker, Y. J. Rosen, L. A. Martinez, and J. L. DuBois, “Quantum computation of three-wave interactions with engineered cubic couplings,” arXiv:2004.06885, Phys. Rev. A 103, 062608 (2021). [5] M. D. Porter, I. Joseph, “Observability of fidelity decay at the Lyapunov rate in few-qubit quantum simulations,” Quantum 6, 799 (2022). [6] M. D. Porter, I. Joseph, “Impact of dynamics, entanglement, and Markovian noise on the fidelity of few-qubit digital quantum simulation,” arXiv:2206.04829, submitted to Quantum (2022).

Scalable and Reliable Platform for AI-based Image Acquisition and Processing (AI)

• Dariusz Makowski (Lodz University of Technology, Department of Microelectronics and Computer Science)

Room: Conference Room 1 (CR1), C Building, 2nd floor Plasma diagnostics is an essential tool to understand and improve plasma stability in fusion devices. It provides useful information for the analysis and understanding of physical phenomena. Infrared (IR) thermography is also important diagnostics applied for machine protection and plasma control, especially for the future fusion devices working with long plasma pulses, such as ITER or DEMO. Real-time monitoring of surface temperatures of the Plasma Facing Components (PFCs) is a main concern to assure a safe long-term operation of the machine and optimize performance of the generated plasma. Fast infrared cameras allow detecting and tracking Thermal Events (TEs) in real-time and therefore assure an efficient thermal load control. A good example is a divertor or the first wall protection system. The image acquisition and processing system is composed of an image detector (digital camera), a frame grabber device that receives a video stream and a device responsible for image acquisition and processing in real-time. Based on IR images, the system could calculate and measure important parameters, such as the maximum temperature of divertor and surface temperature of PFCs, heat-flux or power load. Knowledge of surface emissivity is required to calculate required measurements. The algorithms could be even more complex especially for machines using tungsten materials and working with longer plasma pulses because the emissivity value could change during the pulse and depends on temperature and surface conditions. In this case, algorithms using Artificial Intelligence (AI) and Machine Learning (ML) could be useful and are planned to be used for the future machines. In the case of large-scale fusion devices, the imaging diagnostics is a complex distributed hard real-time system composed of dozens of IR or VIS cameras installed in multiple places of a tokamak or stellarator observing protected machine components, such as divertor or other plasma facing components. Calculating complex measurements in real-time could require a significant processing power and various parallel computation devices, such as multicore CPUs (Central Processing Units), GPUs (Graphics Processing Units) or FPGAs (Field Programable Gate Arrays). In addition, all cameras should be synchronized, and the system should provide measurements in real-time for plasma control and machine protection. The system should be designed with redundancy to improve reliability and it should assure high availability. Acquiring, and processing images from IR cameras requires a flexible hardware platform that provides large enough capability, processing power and synchronization. The architecture of scalable image acquisition and the processing system with an improved reliability will be presented and discussed. The system was developed using the MicroTCA.4 standard that allows to obtain scalability and the requires reliability. The system could be connected to external multicore computers equipped with GPU accelerators that deliver the required processing power. PCI Express interface assures low latency during image acquisition and processing.

Symbiotic Design of Fusion Power Plants

• Lee Margetts (UKAEA)

Room: Conference Room 1 (CR1), C Building, 2nd floor Margetts et al. Current state-of-practice in the design, build and operation of complex engineered systems involves contractual arrangements between many sub-contractors working across an often very complex supply chain. Each company will be responsible for a particular component or subsystem and have its own competences, infrastructure and data management policies. As the need for customisation increases, i.e., the low volume, high value manufacturing required in a first of a kind power plant, there will be a corresponding need to engage with highly skilled specialists. These may be individuals from niche micro-firms or experts embedded in large organisations, who, from the point of view of the wider supply chain, will typically be working in silos. These unconnected islands of knowledge act as firewalls that make it difficult to leverage artificial intelligence in the design process. To address this, there needs to be a paradigm shift in how engineering is organised, moving away from a digitally enabled craft industry towards one that embraces a philosophy of automation. The invention of the world wide web enabled the automation of processes, contracts and data management at a massive scale across a geographically distributed ecosystem; in commerce, banking, media and leisure. The industrial metaverse, the internet in three dimensions, promises to disrupt engineering in the same way. In this presentation, we will show how the metaverse is being used to develop a cyber physical system for power plant design in which design teams connect, collaborate and make increasing use of automation – in near “real-time”. Once the cyber physical system is in operation, it will generate, collect, store, process and consume data; encapsulating knowledge related to fusion power plant design. Our vision is for artificial intelligence to use this knowledgebase to learn how to design complex systems, enabling computers to work as partners with humans in a new era of symbiotic design.

Virtual KSTAR and potential AI/ML applications in the digital twin platform (Physics/Engineering)

• Chanyoung Lee (Korea Institute of Fusion Energy)

Room: Conference Room 1 (CR1), C Building, 2nd floor The transition from present day tokamaks to DEMO reactor will pose great scientific and technological challenges. As a way to overcome those challenges, we have launched the development of the Virtual-KSTAR (V-KSTAR), which is based on digital twin technology. It is aiming to establish a unified machine/fusion data framework and simulation workflows. By elevating the maturity of the digital twin technology through analyzing and predicting tokamak experiments, V-KSTAR would become a crucial step toward the design and construction of the Korean DEMO reactor. Over the past years, a significant progress has been made in the development of V-KSTAR, including the conversion of legacy data and the implementation of workflows which are designed for specific experimental analyses. Initially, the focus has been on the between-shot analysis of KSTAR experiments, facilitating the validation of simulation codes and aiding experimental analysis to improve machine operation. To enhance the performance of simulation codes, we have adopted parallelization techniques and exploited GPU-based acceleration on modern supercomputing environment. However, these advancements alone turned out to be insufficient to realize a comprehensive digital twin. Consequently, incorporating additional acceleration techniques such as artificial intelligence (AI) and machine learning (ML) is indeed essential. In this presentation, we will report the current status of the V-KSTAR development, outlining our plans for the integration of AI and ML. We will discuss strategies for training the models, including the acceleration of simulation codes and the acquisition of simulation data. Furthermore, we will discuss broader aspects of AI/ML technologies, which may contribute to the realization of more advanced fusion digital twin in future.

Data reactor meets fusion reactor: a computational infrastructure for fusion study (Enabling Infrastructure)

• Fei Yang (Zhejiang Lab)

Room: Conference Room 1 (CR1), C Building, 2nd floor The study of nuclear fusion requires a massive amount of computing resource with highly divergent computing paradigm, including simulation, diagnostic, plasma control, AI computing, etc. To achieve the best performance, various tasks should be implemented on heterogeneous computing devices, typically CPUs and GPUs. The heterogeneity on the computing resources and computing paradigms brings obstacle for the researchers to implement and execute their computing programs efficiently. Intelligent computing data reactor is a foundational computing infrastructure constructed by Zhejiang Lab to support scientific research. It aggregates multiple heterogeneous computing clusters which are suitable for high performance scientific computing tasks or AI computing tasks, respectively. The management of the resources and the scheduling of computing tasks are supported by ZJLab ALkaid, an operating system across multiple heterogeneous clusters. Moreover, ZJLab Alkaid encapsulates hundreds of computing frameworks, software modules and computing applications, which enables several domain specific computing platforms, including computational material, computational astronomy, computational genomics, etc. The data reactor integrates the computing hardware resources, the computing software stacks, and the computing service platforms to support cross-disciplinary study. By analogy to fusion reactor, the data reactor makes a reaction of computation to produce research outputs on the basis of data, algorithms, computing resources and domain knowledge. This talk aims to introduce the data reactor to the audience with fusion study background and discuss the progress and perspectives of the infrastructure on fusion study.

Intro to Special Session

• Cristina Rea (Massachusetts Institute of Technology)

Room: Conference Room 1 (CR1), C Building, 2nd floor

Magnetic Control of Tokamak Plasmas through Deep Reinforcement Learning (AI)

• Brendan Tracey (Google DeepMind)

Room: Conference Room 1 (CR1), C Building, 2nd floor Reinforcement learning (RL) is a promising technology for the future of fusion power. A key challenge is to stabilize and regulate the plasma position and shape via magnetic fields generated by a set of control coils. This talk discusses our efforts to generate magnetic controllers using deep reinforcement learning. We train controllers on a Grad-Schafranov based simulator and then deploy the learned controller on experiments on the Tokamak à Configuration Variable (TCV). We show successful stabilization of a diverse set of plasma configurations, and discuss strategies to accelerate training time and improved performance.

Large Language Models as Tools for Searching and Explaining Tokamak Shot Logs (AI)

• Allen Wang (MIT)

Room: Conference Room 1 (CR1), C Building, 2nd floor While the quantitative data generated by tokamaks is invaluable, tokamak operations also generate another, often underutilized data stream: text logs written by experimental operators. In this work, we leverage these extensive text logs by employing Retrieval-Augmented Generation (RAG) with state-of-the-art large language models (LLMs) to create chat-bot instances that can answer questions using knowledge recorded in these historical text logs. Instances of this chat-bot were created using text logs from the fusion experiments DIII-D and Alcator C-Mod and deployed for researchers to use. In this talk, we report on the datasets and methodology used to create these chat-bots, along with their performance in three use cases: 1) semantic search of experiments, 2) assisting with device-specific operations, and 3) answering general tokamak questions. As LLMs improve over the coming years, we hope that future iterations of this work will provide increasingly useful assistance for both fusion research and experimental operations.

Fourier Neural Operator for Plasma Emulation in Simulation and Experiment (AI)

• Vignesh Gopakumar (UK Atomic Energy Authority)

Room: Conference Room 1 (CR1), C Building, 2nd floor Predicting the evolution of plasma instabilities and turbulence within a tokamak power plant is essential for achieving sustainable fusion. Efficiently forecasting the spatio-temporal evolution of plasma enables rapid iteration over design and control strategies for both current tokamak devices and future power plants. However, traditional numerical solvers for modelling plasma evolution are computationally expensive and require hours of processing on supercomputers. In this study, we demonstrate the application of an AI framework called Fourier Neural Operators (FNO) to accurately predict plasma evolution in both simulation and experimental domains. Our research shows that FNO provides a remarkable speedup of six orders of magnitude compared to traditional solvers when predicting plasma blob dynamics simulated from fluid models in slab geometry. Despite this significant speed improvement, FNO maintains reasonable accuracy, especially across short temporal domains. A modified version of FNO is developed to allow for solving multi-variable Partial Differential Equations, capturing the interdependence among different variables within a single model. Furthermore, FNOs exhibit the ability to predict plasma evolution using real-world experimental data obtained from cameras positioned within the MAST tokamak. The predictive power extends up to six milliseconds ahead, making it suitable for real-time plasma evolution monitoring. Additionally, we showcase FNO's proficiency in forecasting plasma shape and the general structure of the filaments observed during plasma shots under study. FNO emerges as a promising surrogate modelling approach due to its quick training and inference times, as well as its ability to perform zero-shot super-resolution (i.e. produce higher resolution solutions without any further training), providing discretization invariance across the models. To address predictive uncertainty, we incorporate conformal prediction over the FNO output, allowing statistically guaranteed error bars across the high-dimensional space of field evolution. In this work, we also explore the utility of PDE residuals for evaluating trained emulators, offering valuable insights into the model's performance. Moreover, we investigate the application of state-space-based neural operators for emulating the long-term evolution of fluxes associated with gyrokinetic turbulence, originally modelled using GX. Overall, our research presents FNO as a powerful alternative for emulating plasma evolution, paving the way for enhanced efficiency in emulating fusion power plant design and control strategies.

Cats, crowds and other considerations for learning with limited labelled data (AI)

• Veronika Cheplygina (IT University of Copenhagen)

Room: Conference Room 1 (CR1), C Building, 2nd floor Machine learning has vast potential in medical image analysis, improving possibilities for early diagnosis and prognosis of disease. Algorithms typically need large amounts of representative, annotated examples for good performance, which may be difficult to achieve, for example due to differences between image acquisition procedures, or the time and effort involved in annotation. To address these problems, several approaches have been proposed, which are either aimed at adapting to use other types of annotated data, and or at gathering annotations more efficiently. In this talk I will highlight two such approaches: transfer learning from natural images such as cats, and crowdsourcing by annotators without medical expertise. I will also discuss more general issues we face we as a community face when addressing such problems.

Closing

• Palak Jain (IAEA)

Room: Conference Room 1 (CR1), C Building, 2nd floor