Neural Operators Learn the Local Physics of Magnetohydrodynamics
- URL: http://arxiv.org/abs/2404.16015v1
- Date: Wed, 24 Apr 2024 17:48:38 GMT
- Title: Neural Operators Learn the Local Physics of Magnetohydrodynamics
- Authors: Taeyoung Kim, Youngsoo Ha, Myungjoo Kang,
- Abstract summary: Magnetohydrodynamics (MHD) plays a pivotal role in describing the dynamics of plasma and conductive fluids.
Recent advances introduce neural operators like the Fourier Neural Operator (FNO) as surrogate models for traditional numerical analyses.
This study explores a modified Flux Fourier neural operator model to approximate the numerical flux of ideal MHD.
- Score: 6.618373975988337
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Magnetohydrodynamics (MHD) plays a pivotal role in describing the dynamics of plasma and conductive fluids, essential for understanding phenomena such as the structure and evolution of stars and galaxies, and in nuclear fusion for plasma motion through ideal MHD equations. Solving these hyperbolic PDEs requires sophisticated numerical methods, presenting computational challenges due to complex structures and high costs. Recent advances introduce neural operators like the Fourier Neural Operator (FNO) as surrogate models for traditional numerical analyses. This study explores a modified Flux Fourier neural operator model to approximate the numerical flux of ideal MHD, offering a novel approach that outperforms existing neural operator models by enabling continuous inference, generalization outside sampled distributions, and faster computation compared to classical numerical schemes.
Related papers
- Magnetic Hysteresis Modeling with Neural Operators [0.7817677116789855]
This paper proposes neural operators for modeling laws that exhibit magnetic by learning a mapping between magnetic fields.
Two prominent neural operators -- deep operator network and Fourier neural operator -- are employed to predict novel first-order reversal curves and minor loops.
arXiv Detail & Related papers (2024-07-03T16:45:45Z) - Equivariant Graph Neural Operator for Modeling 3D Dynamics [148.98826858078556]
We propose Equivariant Graph Neural Operator (EGNO) to directly models dynamics as trajectories instead of just next-step prediction.
EGNO explicitly learns the temporal evolution of 3D dynamics where we formulate the dynamics as a function over time and learn neural operators to approximate it.
Comprehensive experiments in multiple domains, including particle simulations, human motion capture, and molecular dynamics, demonstrate the significantly superior performance of EGNO against existing methods.
arXiv Detail & Related papers (2024-01-19T21:50:32Z) - Machine learning force-field models for metallic spin glass [4.090038845129619]
We present a scalable machine learning framework for dynamical simulations of metallic spin glasses.
A Behler-Parrinello type neural-network model is developed to accurately and efficiently predict electron-induced local magnetic fields.
arXiv Detail & Related papers (2023-11-28T17:12:03Z) - 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) - Magnetohydrodynamics with Physics Informed Neural Operators [2.588973722689844]
We explore the use of AI to accelerate the modeling of complex systems at a fraction of the computational cost of methods.
We present the first application of physics informed neural operators to model 2D incompressible magnetohydrodynamics simulations.
arXiv Detail & Related papers (2023-02-13T19:00:00Z) - Conditional Generative Models for Simulation of EMG During Naturalistic
Movements [45.698312905115955]
We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms.
We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
arXiv Detail & Related papers (2022-11-03T14:49:02Z) - NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural
Radiance Fields [65.07940731309856]
Deep learning has shown great potential for modeling the physical dynamics of complex particle systems such as fluids.
In this paper, we consider a partially observable scenario known as fluid dynamics grounding.
We propose a differentiable two-stage network named NeuroFluid.
It is shown to reasonably estimate the underlying physics of fluids with different initial shapes, viscosity, and densities.
arXiv Detail & Related papers (2022-03-03T15:13:29Z) - EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting.
We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
arXiv Detail & Related papers (2022-02-21T18:59:03Z) - Moser Flow: Divergence-based Generative Modeling on Manifolds [49.04974733536027]
Moser Flow (MF) is a new class of generative models within the family of continuous normalizing flows (CNF)
MF does not require invoking or backpropagating through an ODE solver during training.
We demonstrate for the first time the use of flow models for sampling from general curved surfaces.
arXiv Detail & Related papers (2021-08-18T09:00:24Z) - Neural Ordinary Differential Equations for Data-Driven Reduced Order
Modeling of Environmental Hydrodynamics [4.547988283172179]
We explore the use of Neural Ordinary Differential Equations for fluid flow simulation.
Test problems we consider include incompressible flow around a cylinder and real-world applications of shallow water hydrodynamics in riverine and estuarine systems.
Our findings indicate that Neural ODEs provide an elegant framework for stable and accurate evolution of latent-space dynamics with a promising potential of extrapolatory predictions.
arXiv Detail & Related papers (2021-04-22T19:20:47Z) - Quaternion Factorization Machines: A Lightweight Solution to Intricate
Feature Interaction Modelling [76.89779231460193]
factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering.
We propose the quaternion factorization machine (QFM) and quaternion neural factorization machine (QNFM) for sparse predictive analytics.
arXiv Detail & Related papers (2021-04-05T00:02:36Z)
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.