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20182026
most citedPhysics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

1.1k citations · 1.1k across the 9 of their papers we have counts for

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8 papers · 1 filter

stat.ML2026

GenPANIS: A Latent-Variable Generative Framework for Forward and Inverse PDE Problems in Multiphase Media

Matthaios Chatzopoulos, Phaedon-Stelios Koutsourelakis

Inverse problems and inverse design in multiphase media, i.e., recovering or engineering microstructures to achieve target macroscopic responses, require operating on discrete-valu…

stat.ML2025

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…

stat.ML2021

Self-supervised optimization of random material microstructures in the small-data regime

Maximilian Rixner, Phaedon-Stelios Koutsourelakis

While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials community, fewer efforts have taken into consider…

stat.ML20212 cited

Physics-aware, probabilistic model order reduction with guaranteed stability

Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis

Given (small amounts of) time-series' data from a high-dimensional, fine-grained, multiscale dynamical system, we propose a generative framework for learning an effective, lower-di…

stat.ML2020

A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables

Maximilian Rixner, Phaedon-Stelios Koutsourelakis

The data-centric construction of inexpensive surrogates for fine-grained, physical models has been at the forefront of computational physics due to its significant utility in many-…

stat.ML2019

A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime

Constantin Grigo, Phaedon-Stelios Koutsourelakis

The automated construction of coarse-grained models represents a pivotal component in computer simulation of physical systems and is a key enabler in various analysis and design ta…