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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…
Efficient Bayesian multi-fidelity inverse analysis for expensive and non-differentiable physics-based simulations in high stochastic dimensions
Jonas Nitzler, Bugrahan Z. Temür, Phaedon-Stelios Koutsourelakis +1
High-dimensional Bayesian inverse analysis (dim >> 100) is mostly unfeasible for computationally demanding, nonlinear physics-based high-fidelity (HF) models. Usually, the use of m…
Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data
Maximilian Stupp, P. S. Koutsourelakis
Coarse-grained (CG) models provide an effective route to reducing the complexity of molecular simulations (MD), but conventional approaches depend heavily on long all-atom MD traje…
The Illusion of Fit: Spatially Resolved Assessment of Constitutive Model Validity in Elastography and Physics-Based Inverse Problems
Vincent C. Scholz, P. S. Koutsourelakis
Inferring the mechanical properties of soft tissues from measured deformations is a fundamental challenge in elastography. A rarely examined assumption underlying existing approach…
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…