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20162026
most citedPhysics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

1.1k citations · 1.1k across the 24 of their papers we have counts for

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stat.ML2024

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…

stat.ML2024★ 11 cited

Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media

Matthaios Chatzopoulos, Phaedon-Stelios Koutsourelakis

We propose Physics-Aware Neural Implicit Solvers (PANIS), a novel, data-driven framework for learning surrogates for parametrized Partial Differential Equations (PDEs). It consists…