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Shengyu Chen

2 accepted papers

2026

Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators

IJCAI 2026

Partial differential equations (PDEs) are central to scientific modeling. Nowadays, modern workflows increasingly rely on learning-based components to support model reuse, inference, and integration across large computational processes. Despite the emergence of various physics-aware data-driven appr

Cited by 0Scholar
2022

Physics guided neural networks for spatio-temporal super-resolution of turbulent flows

UAI 2022poster

Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers. Low-resolution large eddy simulation (LES) is a popular alternative, but it is unable to capture all of the scales of turbulent transport accurately. Reconst…

Cited by 29SourcePDFScholar