Plasma Surrogate Modelling using Fourier Neural Operators
Caltech（加州理工学院）
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi, Zongyi Li, Ander Gray, Daniel Brennand, Nitesh Bhatia, Gregory Stathopoulos, Matt Kusner, Marc Peter Deisen… 展开作者
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi, Zongyi Li, Ander Gray, Daniel Brennand, Nitesh Bhatia, Gregory Stathopoulos, Matt Kusner, Marc Peter Deisenroth, Anima Anandkumar, JOREK Team, MAST Team
Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma rapidly and accurately allow us to quickly iterate over design and control strategies on current Tokamak devices and future reactors. Modelling plasma evolution using numerical solvers is often expensive, consuming many hours on supercomputers, and hence, we need alternative inexpensive surrogate models. We demonstrate accurate predictions of plasma evolution both in simulation and experimental domains using deep learning-based surrogate modelling tools, viz., Fourier Neural Operators (FNO). We show that FNO has a speedup of six orders of magnitude over traditional solvers in predicting the plasma dynamics simulated from magnetohydrodynamic models, while maintaining a high accuracy (MSE in the normalised domain $\approx$ $10^{-5}$). Our modified version of the FNO is capable of solving multi-variable Partial Differential Equations (PDE), and can capture the dependence among the different variables in a single model. FNOs can also predict plasma evolution on real-world experimental data observed by the cameras positioned within the MAST Tokamak, i.e., cameras looking across the central solenoid and the divertor in the Tokamak. We show that FNOs are able to accurately forecast the evolution of plasma and have the potential to be deployed for real-time monitoring. We also illustrate their capability in forecasting the plasma shape, the locations of interactions of the plasma with the central solenoid and the divertor for the full (available) duration of the plasma shot within MAST. The FNO offers a viable alternative for surrogate modelling as it is quick to train and infer, and requires fewer data points, while being able to do zero-shot super-resolution and getting high-fidelity solutions.
Surrogates for Physics-based and Data-driven Modelling of Parametric Systems: Review and New Perspectives
Laboratori de Càlcul Numeric (LaCàN), E.T.S. de Ingeniería de Caminos, Canales y Puertos, Universitat Politècnica de Catalunya - BarcelonaTech (UPC)（计算数值实验室（LaCàN），土木工程系，加泰罗尼亚理工大学-巴塞罗那科技大学）；Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE)（国际数值工程方法中心）
A Fast and Generalizable Fourier Neural Operator-Based Surrogate for Melt-Pool Prediction in Laser Processing
Laboratory for Advanced Materials Processing, Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科学与技术实验室先进材料加工实验室）；Terra Quantum AG（Terra Quantum公司）
A physics-encoded Fourier neural operator approach for surrogate modeling of divergence-free stress fields in solids
Max-Planck Institute for Sustainable Materials（马克斯·普朗克可持续材料研究所）；Max-Planck Institute for Dynamics of Complex Technical Systems（马克斯·普朗克复杂技术系统动力学研究所）；RWTH Aachen University（亚琛工业大学）
Neural Fourier Modelling: A Highly Compact Approach to Time-Series Analysis
The University of Auckland（奥克兰大学）；School of Computer Science（计算机科学学院）；Acoustics Vibration Research Centre（声学与振动研究中心）；Mechanical Mechatronics Engineering（机械与机电工程学院）；Strong AI Lab（强人工智能实验室）
Modelling magnetic material properties with uncertainty-aware neural networks
University of Cambridge（剑桥大学）；ETH Zurich（苏黎世联邦理工学院）；University of Tokyo（东京大学）；National Institute for Materials Science（国家材料科学研究所）；Max Planck Institute for Intelligent Systems（智能系统马克斯·普朗克研究所）
5D Neural Surrogates for Nonlinear Gyrokinetic Simulations of Plasma Turbulence
ELLIS Unit, LIT AI Lab, Institute for Machine Learning, JKU Linz（ELLIS单元、LIT AI实验室、林茨约翰内斯·开普勒大学机器学习研究所）；UKAEA (United Kingdom Atomic Energy Authority)（英国原子能管理局）；Emmi AI GmbH（Emmi AI有限公司）
