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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.03119 |
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| _version_ | 1866908918152691712 |
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| author | Thun, Timo Merlo, Andrea Conlin, Rory Panici, Dario Böckenhoff, Daniel |
| author_facet | Thun, Timo Merlo, Andrea Conlin, Rory Panici, Dario Böckenhoff, Daniel |
| contents | We present a novel approach to compute three-dimensional Magnetohydrodynamic equilibria by parametrizing Fourier modes with artificial neural networks and compare it to equilibria computed by conventional solvers. The full nonlinear global force residual across the volume in real space is then minimized with first order optimizers. Already,we observe competitive computational cost to arrive at the same minimum residuals computed by existing codes. With increased computational cost,lower minima of the residual are achieved by the neural networks,establishing a new lower bound for the force residual. We use minimally complex neural networks,and we expect significant improvements for solving not only single equilibria with neural networks,but also for computing neural network models valid over continuous distributions of equilibria. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03119 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving ideal MHD equilibrium accuracy with physics-informed neural networks Thun, Timo Merlo, Andrea Conlin, Rory Panici, Dario Böckenhoff, Daniel Machine Learning Artificial Intelligence Plasma Physics We present a novel approach to compute three-dimensional Magnetohydrodynamic equilibria by parametrizing Fourier modes with artificial neural networks and compare it to equilibria computed by conventional solvers. The full nonlinear global force residual across the volume in real space is then minimized with first order optimizers. Already,we observe competitive computational cost to arrive at the same minimum residuals computed by existing codes. With increased computational cost,lower minima of the residual are achieved by the neural networks,establishing a new lower bound for the force residual. We use minimally complex neural networks,and we expect significant improvements for solving not only single equilibria with neural networks,but also for computing neural network models valid over continuous distributions of equilibria. |
| title | Improving ideal MHD equilibrium accuracy with physics-informed neural networks |
| topic | Machine Learning Artificial Intelligence Plasma Physics |
| url | https://arxiv.org/abs/2507.03119 |