Physics-constrained 3D Convolutional Neural Networks for Electrodynamics
Not on the map. Why?
Abstract (arXiv)
We present a physics-constrained neural network (PCNN) approach to solving Maxwell's equations for the electromagnetic fields of intense relativistic charged particle beams. We create a 3D convolutional PCNN to map time-varying current and charge densities J(r,t) and p(r,t) to vector and scalar potentials A(r,t) and V(r,t) from which we generate electromagnetic fields according to Maxwell's equations: B=curl(A), E=-div(V)-dA/dt. Our PCNNs satisfy hard constraints, such as div(B)=0, by construction. Soft constraints push A and V towards satisfying the Lorenz gauge.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 9 of its 68 references that are in the map. First posted on arXiv 31 Jan 2023.
Builds on
9 of 68 references.
References · 9
- 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
- 2021Learning the solution operator of parametric partial differential equations with physics-informed DeepOnetscited by 540
- 2022On the influence of over-parameterization in manifold based surrogates and deep neural operatorscited by 29
- 2021Metasurface-Enabled On-Chip Multiplexed Diffractive Neural Networks in the Visiblecited by 18
- 2020Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imagingcited by 8
- 2020Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Acceleratorscited by 7
- 2021High-fidelity Prediction of Megapixel Longitudinal Phase-space Images of Electron Beams using Encoder-Decoder Neural Networkscited by 7
- 2021Adaptive Machine Learning for Time-Varying Systems: Low Dimensional Latent Space Tuningcited by 3
- 2022Mixed Diagnostics for Longitudinal Properties of Electron Bunches in a Free-Electron Lasercited by 1
Built on by
Citing papers per year: 2024: 1; 2025: 0; 2026: 2.
3 papers.
- 2024A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle acceleratorscited by 8
- 2026Bidirectional Autoregressive Latent Diffusion for Forward and Inverse Magnetohydrodynamicscited by 1
- 2026Fast Reconstruction of Exact Maxwell Dynamics from Sparse Datacited by 0
- 2026Bidirectional Autoregressive Latent Diffusion for Forward and Inverse Magnetohydrodynamics28 Jun 2026
- 2026Fast Reconstruction of Exact Maxwell Dynamics from Sparse Data19 May 2026
- 2024A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators19 Mar 2024