Hybridizing Physics and Neural ODEs for Predicting Plasma Inductance Dynamics in Tokamak Fusion Reactors

• URL: http://arxiv.org/abs/2310.20079v1
• Date: Mon, 30 Oct 2023 23:25:54 GMT
• Title: Hybridizing Physics and Neural ODEs for Predicting Plasma Inductance Dynamics in Tokamak Fusion Reactors
• Authors: Allen M. Wang, Darren T. Garnier, and Cristina Rea
• Abstract summary: We train both physics-based and neural network models on data from the Alcator C-Mod fusion reactor. We find that a model that combines physics-based equations with a neural ODE performs better than both existing physics-motivated ODEs and a pure neural ODE model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While fusion reactors known as tokamaks hold promise as a firm energy source, advances in plasma control, and handling of events where control of plasmas is lost, are needed for them to be economical. A significant bottleneck towards applying more advanced control algorithms is the need for better plasma simulation, where both physics-based and data-driven approaches currently fall short. The former is bottle-necked by both computational cost and the difficulty of modelling plasmas, and the latter is bottle-necked by the relative paucity of data. To address this issue, this work applies the neural ordinary differential equations (ODE) framework to the problem of predicting a subset of plasma dynamics, namely the coupled plasma current and internal inductance dynamics. As the neural ODE framework allows for the natural inclusion of physics-based inductive biases, we train both physics-based and neural network models on data from the Alcator C-Mod fusion reactor and find that a model that combines physics-based equations with a neural ODE performs better than both existing physics-motivated ODEs and a pure neural ODE model.

Related papers

• Latent Space Energy-based Neural ODEs [73.01344439786524]
  This paper introduces a novel family of deep dynamical models designed to represent continuous-time sequence data. We train the model using maximum likelihood estimation with Markov chain Monte Carlo. Experiments on oscillating systems, videos and real-world state sequences (MuJoCo) illustrate that ODEs with the learnable energy-based prior outperform existing counterparts.
  arXiv  Detail & Related papers  (2024-09-05T18:14:22Z)
• Data-driven local operator finding for reduced-order modelling of plasma systems: I. Concept and verifications [2.9320342785886973]
  Reduced-order plasma models can efficiently predict plasma behavior across various settings and configurations. We introduce the "Phi Method" in this two-part article. Part I presents this novel algorithm, which employs constrained regression on a candidate term library. Part II will delve into the method's application for parametric dynamics discovery.
  arXiv  Detail & Related papers  (2024-03-03T14:50:15Z)
• Plasma Surrogate Modelling using Fourier Neural Operators [57.52074029826172]
  Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. We demonstrate accurate predictions of evolution plasma using deep learning-based surrogate modelling tools, viz., 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. FNOs can also predict plasma evolution on real-world experimental data observed by the cameras positioned within the MAST Tokamak.
  arXiv  Detail & Related papers  (2023-11-10T10:05:00Z)
• Learning the dynamics of a one-dimensional plasma model with graph neural networks [0.0]
  We show that our model learns the kinetic plasma dynamics of the one-dimensional plasma model. We compare the performance against the original plasma model in terms of run-time, conservation laws, and temporal evolution of key physical quantities.
  arXiv  Detail & Related papers  (2023-10-26T17:58:12Z)
• MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling [3.900623554490941]
  We propose a novel architecture for generating model-integrated neural networks (MINN) MINN allows integration on the level of learning physics-based dynamics of the system. We apply the proposed neural network architecture to model the electrochemical dynamics of lithium-ion batteries.
  arXiv  Detail & Related papers  (2023-04-27T09:11:40Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition [67.46012350241969]
  This paper proposes a general acceleration methodology called NeuralStagger. It decomposing the original learning tasks into several coarser-resolution subtasks. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations.
  arXiv  Detail & Related papers  (2023-02-20T19:36:52Z)
• Physics-based parameterized neural ordinary differential equations: prediction of laser ignition in a rocket combustor [2.227105438439618]
  We present a physics-based data-driven framework for reduced-order modeling of laser ignition in a model rocket combustor. Deep neural networks are embedded as functions of high-dimensional parameters of laser ignition to predict various terms in a 0D flow model.
  arXiv  Detail & Related papers  (2023-02-16T23:56:51Z)
• Data-driven modeling of Landau damping by physics-informed neural networks [4.728411962159049]
  We construct a multi-moment fluid model with an implicit fluid closure included in the neural network using machine learning. The model reproduces the time evolution of the electric field energy, including its damping rate, and the plasma dynamics from the kinetic simulations. This work sheds light on the accurate and efficient modeling of large-scale systems, which can be extended to complex multiscale laboratory, space, and astrophysical plasma physics problems.
  arXiv  Detail & Related papers  (2022-11-02T10:33:38Z)
• Unsupervised Discovery of Inertial-Fusion Plasma Physics using Differentiable Kinetic Simulations and a Maximum Entropy Loss Function [77.34726150561087]
  We create a differentiable solver for the plasma kinetics 3D partial-differential-equation and introduce a domain-specific objective function. We apply this framework to an inertial-fusion relevant configuration and find that the optimization process exploits a novel physical effect.
  arXiv  Detail & Related papers  (2022-06-03T15:27:33Z)
• Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations [62.81701992551728]
  We present a physics-informed framework for solving time-dependent partial differential equations. Our model utilizes discrete cosine transforms to encode spatial and recurrent neural networks. We show experimental results on the Taylor-Green vortex solution to the Navier-Stokes equations.
  arXiv  Detail & Related papers  (2022-02-24T20:46:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.