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cs.LG2024★ 15 cited
Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint
Hanyang Wang, Hao Zhou, Sibo Cheng
Despite the success of various methods in addressing the issue of spatial reconstruction of dynamical systems with sparse observations, spatio-temporal prediction for sparse fields…
cs.LG2024★ 33 cited
Multi-fidelity physics constrained neural networks for dynamical systems
Hao Zhou, Sibo Cheng, Rossella Arcucci
Physics-constrained neural networks are commonly employed to enhance prediction robustness compared to purely data-driven models, achieved through the inclusion of physical constra…