54 citations · 163 across the 20 of their papers we have counts for
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Single-condition neural solvers encode transferable response spaces for parametric differential equations
Wenbo Cao, Weiwei Zhang
Operator learning for parametric partial differential equations (PDEs) typically builds global models over prescribed domains, requiring cross-condition data or costly physics-cons…
Physics-informed neural networks for shock capturing in inviscid flows around an airfoil
Jiahao Song, Wenbo Cao, Weiwei Zhang
Physics-informed neural networks (PINNs) have shown remarkable prospects in solving forward and inverse problems involving partial differential equations (PDEs). However, PINNs sti…
Verified residual-specific explicit derivative kernels for physics-informed learning and discretized PDE adjoints
Wenbo Cao, Zhe Lu, Weiwei Zhang
Derivative computation is central to scientific computing, from space-time derivatives in physics-informed neural networks (PINNs) to residual Jacobian actions and discrete-adjoint…
Optimization-Based Discovery of A Non-Attracting Flow State in An Oscillating-Cylinder Wake
Daiwei Dong, Wenbo Cao, Wei Suo +2
In the flow past a stationary circular cylinder, the classical Karman vortex street arises from a Hopf bifurcation of the steady flow at the critical Reynolds number. Although this…
Solving compressible Navier-Stokes equations using the feature-enhanced neural network
Jiahao Song, Wenbo Cao, Weiwei Zhang
Physics-informed neural networks (PINNs) have shown remarkable prospects in solving partial differential equations (PDEs) involving fluid mechanics. However, the method has so far…
Linearized subspace refinement framework to expose hidden accuracy in trained neural networks
Wenbo Cao, Weiwei Zhang
Neural networks trained by gradient-based methods often exhibit optimization-induced accuracy plateaus in scientific machine learning tasks. We present Linearized Subspace Refineme…