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Embedded Model Form Uncertainty Quantification with Measurement Noise for Bayesian Model Calibration
Daniel Andrés Arcones, Martin Weiser, Phaedon-Stelios Koutsourelakis +1
A key factor in ensuring the accuracy of computer simulations that model physical systems is the proper calibration of their parameters based on real-world observations or experime…
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…
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…