Neural Operator for Accelerating Coronal Magnetic Field Model

• URL: http://arxiv.org/abs/2405.12754v2
• Date: Thu, 27 Jun 2024 02:03:21 GMT
• Title: Neural Operator for Accelerating Coronal Magnetic Field Model
• Authors: Yutao Du, Qin Li, Raghav Gnanasambandam, Mengnan Du, Haimin Wang, Bo Shen,
• Abstract summary: Magnetohydrodynamics (MHD) simulations help model these interactions but are extremely time-consuming (usually on a scale of days) Our research applies the Fourier Neural Operator (FNO) to accelerate the magnetic field modeling, specifically, the Bifrost MHD model. Physics analysis confirms that TFNO is reliable and capable of accelerating MHD simulations with high precision. This advancement improves efficiency in data handling, enhances predictive capabilities, and provides a better understanding of magnetic topologies.
• Score: 17.256941005824576
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Studying the sun's outer atmosphere is challenging due to its complex magnetic fields impacting solar activities. Magnetohydrodynamics (MHD) simulations help model these interactions but are extremely time-consuming (usually on a scale of days). Our research applies the Fourier Neural Operator (FNO) to accelerate the coronal magnetic field modeling, specifically, the Bifrost MHD model. We apply Tensorized FNO (TFNO) to generate solutions from partial differential equations (PDEs) over a 3D domain efficiently. TFNO's performance is compared with other deep learning methods, highlighting its accuracy and scalability. Physics analysis confirms that TFNO is reliable and capable of accelerating MHD simulations with high precision. This advancement improves efficiency in data handling, enhances predictive capabilities, and provides a better understanding of magnetic topologies.

Related papers

• Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration [5.77922305904338]
  Surrogate modeling with data-driven machine learning has become a promising alternative to accelerate physics-based simulations. We have developed a nested Fourier-DeepONet by combining the expressiveness of the FNO with the modularity of a deep operator network (DeepONet) This new framework is twice as efficient as a nested FNO for training and has at least 80% lower GPU memory requirement.
  arXiv  Detail & Related papers  (2024-09-25T02:58:45Z)
• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• KFD-NeRF: Rethinking Dynamic NeRF with Kalman Filter [49.85369344101118]
  We introduce KFD-NeRF, a novel dynamic neural radiance field integrated with an efficient and high-quality motion reconstruction framework based on Kalman filtering. Our key idea is to model the dynamic radiance field as a dynamic system whose temporally varying states are estimated based on two sources of knowledge: observations and predictions. Our KFD-NeRF demonstrates similar or even superior performance within comparable computational time and state-of-the-art view synthesis performance with thorough training.
  arXiv  Detail & Related papers  (2024-07-18T05:48:24Z)
• Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone [0.7329200485567827]
  We propose U-Shaped Adaptive Fourier Neural Operators (U-AFNO), a machine learning (ML) model inspired by recent advances in neural operator learning. We use U-AFNOs to learn the dynamics mapping the field at a current time step into a later time step. Our model reproduces the key micro-structure statistics and QoIs with a level of accuracy on-par with the high-fidelity numerical solver.
  arXiv  Detail & Related papers  (2024-06-24T20:13:23Z)
• Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamics [0.0]
  This work introduces the Multiple-Input Fourier Neural Operator (MIFNO) to deal with structured 3D fields representing material properties. The MIFNO is applied to the problem of elastic wave propagation in the Earth's crust. It is trained on the HEMEWS-3D database containing 30000 earthquake simulations in different heterogeneous domains.
  arXiv  Detail & Related papers  (2024-04-15T20:07:44Z)
• Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere [53.63505583883769]
  We introduce Spherical FNOs (SFNOs) for learning operators on spherical geometries. SFNOs have important implications for machine learning-based simulation of climate dynamics.
  arXiv  Detail & Related papers  (2023-06-06T16:27:17Z)
• Forecasting subcritical cylinder wakes with Fourier Neural Operators [58.68996255635669]
  We apply a state-of-the-art operator learning technique to forecast the temporal evolution of experimentally measured velocity fields. We find that FNOs are capable of accurately predicting the evolution of experimental velocity fields throughout the range of Reynolds numbers tested.
  arXiv  Detail & Related papers  (2023-01-19T20:04:36Z)
• Factorized Fourier Neural Operators [77.47313102926017]
  The Factorized Fourier Neural Operator (F-FNO) is a learning-based method for simulating partial differential equations. We show that our model maintains an error rate of 2% while still running an order of magnitude faster than a numerical solver.
  arXiv  Detail & Related papers  (2021-11-27T03:34:13Z)
• U-FNO -- an enhanced Fourier neural operator based-deep learning model for multiphase flow [43.572675744374415]
  We present U-FNO, an enhanced Fourier neural operator for solving the multiphase flow problem. We show that the U-FNO architecture has the advantages of both traditional CNN and original FNO, providing significantly more accurate and efficient performance. The trained U-FNO provides gas saturation and pressure buildup predictions with a 10,000 times speedup compared to traditional numerical simulators.
  arXiv  Detail & Related papers  (2021-09-03T17:52:25Z)
• A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations [69.73803123972297]
  We propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach. The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems.
  arXiv  Detail & Related papers  (2020-12-21T17:39:51Z)

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.