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Mosaic Flow is a novel domain decomposition method designed to scale physics-informed neural PDE solvers to large domains.
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Physics-informed machine learning
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PyAMG: Algebraic Multigrid Solvers in Python
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Finite basis physics-informed neural networks as a Schwarz domain decomposition method
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Scalable communication for high-order stencil computations using CUDA-aware MPI
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Mosaic flows: A transferable deep learning framework for solving PDEs on unseen domains
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When and why PINNs fail to train: A neural tangent kernel perspective
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Multilevel domain decomposition-based architectures for physics-informed neural networks
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Multi-GPU Communication Schemes for Iterative Solvers: When CPUs Are Not in Charge. In Proceedings of the 37th International Conference on Supercomputing (Orlando, FL, USA) (ICS ’23) . Association for Computing Machinery, New York, NY, USA, 192–202
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Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations.. In AAAI Spring Symposium: MLPS . 2002–2041
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