A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions
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Publication:6048429
DOI10.1016/j.jcp.2023.112464arXiv2302.14227MaRDI QIDQ6048429
Shandian Zhe, Ameya D. Jagtap, Robert M. Kirby, Michael Penwarden, George Em. Karniadakis
Publication date: 10 October 2023
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2302.14227
Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Numerical analysis (65-XX)
Related Items (3)
Physics-informed neural networks for approximating dynamic (hyperbolic) PDEs of second order in time: error analysis and algorithms ⋮ An extreme learning machine-based method for computational PDEs in higher dimensions ⋮ Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions
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