1 citations · 1 across the 5 of their papers we have counts for
5 papers
A unified physics-informed generative operator framework for general inverse problems
Gang Bao, Yaohua Zang
Solving inverse problems governed by partial differential equations (PDEs) is central to science and engineering, yet remains challenging when measurements are sparse, noisy, or wh…
Design-GenNO: A Physics-Informed Generative Model with Neural Operators for Inverse Microstructure Design
Yaohua Zang, Phaedon-Stelios Koutsourelakis
Inverse microstructure design plays a central role in materials discovery, yet remains challenging due to the complexity of structure-property linkages and the scarcity of labeled…
DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling
Yaohua Zang, Phaedon-Stelios Koutsourelakis
Solving parametric partial differential equations (PDEs) and associated PDE-based, inverse problems is a central task in engineering and physics, yet existing neural operator metho…
PSP-GEN: Stochastic inversion of the Process-Structure-Property chain in materials design through deep, generative probabilistic modeling
Yaohua Zang, Phaedon-Stelios Koutsourelakis
Inverse material design is a cornerstone challenge in materials science, with significant applications across many industries. Traditional approaches that invert the structure-prop…
Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography
Vincent C. Scholz, Yaohua Zang, Phaedon-Stelios Koutsourelakis
In this paper, we introduce a novel, data-driven approach for solving high-dimensional Bayesian inverse problems based on partial differential equations (PDEs), called Weak Neural…