37 citations · 75 across the 11 of their papers we have counts for
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cs.LG2023★ 37 cited
Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks
Zheyuan Hu, Zekun Shi, George Em Karniadakis +1
Physics-Informed Neural Networks (PINNs) have proven effective in solving partial differential equations (PDEs), especially when some data are available by seamlessly blending data…
cs.LG2023★ 9 cited
Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs
Zheyuan Hu, Zhouhao Yang, Yezhen Wang +2
While physics-informed neural networks (PINNs) have been proven effective for low-dimensional partial differential equations (PDEs), the computational cost remains a hurdle in high…