arXiv Machine Learning

Real-Time Plasma State Prediction via FPGA-Accelerated Quantized Recurrent Probabilistic Neural Networks

The paper presents an end‑to‑end workflow for deploying a recurrent probabilistic neural network (RPNN) on FPGA hardware to enable real‑time plasma state estimation for Tokamak control. By reducing architecture size and applying quantization‑aware training with QKeras, the model is synthesized with hls4ml for a Xilinx Alveo U50 device, achieving deterministic sub‑10 µs single‑timestep latency while staying within all four resource budgets (DSP, LUT, FF, BRAM).

arXiv Machine Learning

Aug 28

Real-time virtual circuits for plasma shape control via neural network emulators: integration and testing in the MAST-U PCS

The paper reports on the integration and testing of neural‑network emulators that predict plasma shape for the MAST‑Upgrade tokamak’s plasma control system. The neural models use plasma current, poloidal field coil currents, and plasma profile parameters to output shape predictions and Jacobians, which are then used by a real‑time C++ inference server to compute virtual circuit matrices and updated coil current requests. The work emphasizes a validation workflow and best practices to build confidence in the AI‑based control framework before experimental deployment.

By Matthew J. Marshall, Edward Jones, Graham J. McArdle, Alasdair Ross, Kamran Pentland, Nicola C. Amorisco, Charles Vincent, Martin Kochan, Colin Hogben, Graham Jones, Adam Stephen, George K. Holt, Adriano Agnello

arXiv AI

Aug 25

AI Surrogate Modeling for Real-Time Tokamak Equilibrium Prediction: Benchmarking Neural Architectures and Validation on EXL-50U

arXiv:2608.23217v1 Announce Type: cross Abstract: Fast and reliable plasma equilibrium prediction is essential for real-time tokamak operation and control, but conventional Grad-Shafranov (GS) solver...

By Guoyang Shi, Zitong Zhang, Siqi Ding, Jianguo Chen, Yapeng Zhang, Jiayi Zhi, Hanyue Zhao, Tianyuan Liu

llms benchmarks

Hugging Face Trending Papers

Aug 24

AI Surrogate Modeling for Real-Time Tokamak Equilibrium Prediction: Benchmarking Neural Architectures and Validation on EXL-50U

Fast and reliable plasma equilibrium prediction is essential for real-time tokamak operation and control, but conventional Grad-Shafranov (GS) solvers are often too costly for real-time deployment. We...

llms benchmarks

arXiv Machine Learning

Jun 16

Towards Data-Efficient Cross-Device Generalization of Grad-Shafranov Equilibria via Transfer Learning Neural Operator

arXiv:2606. 15512v1 Announce Type: new Abstract: Real-time reconstruction of magnetohydrodynamic equilibria is essential for plasma shaping, stability assessment and feedback control in magnetic confinement fusion.

By Jay Phil Yoo, William Howes, Yashika Ghai, Kazuma Kobayashi, Souvik Chakraborty, Syed Bahauddin Alam

fine-tuning benchmarks

arXiv Machine Learning

Jun 10

Spiking Neural Network inference on FPGAs with hls4ml

arXiv:2606. 10008v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) provide a naturally temporal machine-learning framework.

By Barry M. Dillon

arXiv AI

Jul 24

Cycle-Consistent and Uncertainty-Aware Neural Surrogates for Tokamak Edge Plasmas

arXiv:2607. 21407v1 Announce Type: cross Abstract: The boundary and divertor plasma govern how a tokamak exhausts power and particles, setting heat fluxes, target conditions, and the onset of detachment.

By Abdourahmane Diaw, Sebastian De Pascuale, Jae-Sun Park, Ivan Paradela Perez, Jeremy D. Lore, Stefan Dasbach

arXiv Machine Learning

Jul 2

Real-time virtual circuits for plasma shape control via neural network emulators

arXiv:2605. 14939v2 Announce Type: replace-cross Abstract: Reliable position and shape control in tokamak plasmas requires accurate real-time regulation of several strongly coupled shape parameters.

By Alasdair Ross, George K. Holt, Kamran Pentland, Adriano Agnello, Nicola C. Amorisco, Pedro Cavestany, Aran Garrod, Timothy Nunn, Charles Vincent, Graham McArdle

arXiv Machine Learning

Aug 21

Heteroscedastic Neural Surrogate Modeling for Robust and Rapid Bayesian Inference in Fusion Plasma Diagnostics

arXiv:2608. 19377v1 Announce Type: cross Abstract: Bayesian inference via Markov Chain Monte Carlo (MCMC) provides effective parameter estimation, but its real-time application in complex physical systems is hindered by heavy computational bottlenecks and extreme sensitivity to statistical noise.

By Liyun Zhang, Naoya Mamada, Kentaro Sakai, Takeo Hoshi, Toru Aonishi

arXiv AI

Jun 9

Learning Quantized Continuous Controllers for Integer Hardware

arXiv:2511. 07046v4 Announce Type: replace-cross Abstract: Deploying continuous-control reinforcement learning policies on embedded hardware requires meeting tight latency and power budgets.

By Fabian Kresse, Christoph H. Lampert

reinforcement-learning efficiency

arXiv Machine Learning

Aug 3

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml

arXiv:2602. 15751v2 Announce Type: replace-cross Abstract: This paper presents an end-to-end demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which could be used in future high-energy physics experiments.

By Katya Govorkova, Julian Garcia Pardinas, Vladimir Loncar, Victoria Nguyen, Sebastian Schmitt, Marco Pizzichemi, Loris Martinazzoli, Eluned Anne Smith

diffusion efficiency

arXiv Machine Learning

Jun 16

The Machine Learning Approach to Moment Closure Relations for Plasma: A Review

arXiv:2511. 22486v3 Announce Type: replace-cross Abstract: The requirement for large-scale global simulations of plasma is an ongoing challenge in both space and laboratory plasma physics.

By Samuel Burles, Enrico Camporeale

arXiv AI

Jun 2

Computation-Aware Kalman Filtering with Model Selection for Neural Dynamics

arXiv:2606. 01468v1 Announce Type: cross Abstract: Due to their explicit priors and ability to model uncertainty, Bayesian methods have played a major role in dynamical latent variable modeling of single-cell neural recordings.

By JR Huml, Jonathan Wenger, John P. Cunningham

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