1.1k citations · 1.4k across the 43 of their papers we have counts for
44 papers
Embedding Model-form Uncertainty in Probabilistic Calibration of Digital Twins for Bridges
Daniel Andrés Arcones, Martin Weiser, Phaedon-Stelios Koutsourelakis +1
Digital twins of bridges rely on physics-based models to infer full-field structural responses from sparse monitoring data. The reliability of these predictions depends on the cali…
Scalable High-Dimensional Bayesian Field Reconstruction with Finite Elements: Application to 3D Porous Media Flow
Jonas Nitzler, Maximilian Bergbauer, Phaedon-Stelios Koutsourelakis +1
We present a unified, finite-element-native variational inference framework for very high-dimensional Bayesian spatial field reconstruction in physics-based problems governed by pa…
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…
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…
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…
From concrete mixture to structural design -- a holistic optimization procedure in the presence of uncertainties
Atul Agrawal, Erik Tamsen, Phaedon-Stelios Koutsourelakis +1
Designing civil structures such as bridges, dams or buildings is a complex task requiring many synergies from several experts. Each is responsible for different parts of the proces…
Model bias identification for Bayesian calibration of stochastic digital twins of bridges
Daniel Andrés Arcones, Martin Weiser, Phaedon-Stelios Koutsourelakis +1
Simulation-based digital twins must provide accurate, robust and reliable digital representations of their physical counterparts. Quantifying the uncertainty in their predictions p…
Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators
Atul Agrawal, Kislaya Ravi, Phaedon-Stelios Koutsourelakis +1
Constrained optimization of the parameters of a simulator plays a crucial role in a design process. These problems become challenging when the simulator is stochastic, computationa…
Physics-Informed Tensor Basis Neural Network for Turbulence Closure Modeling
Leon Riccius, Atul Agrawal, Phaedon-Stelios Koutsourelakis
Despite the increasing availability of high-performance computational resources, Reynolds-Averaged Navier-Stokes (RANS) simulations remain the workhorse for the analysis of turbule…
A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty
Atul Agrawal, Phaedon-Stelios Koutsourelakis
We propose a data-driven, closure model for Reynolds-averaged Navier-Stokes (RANS) simulations that incorporates aleatoric, model uncertainty. The proposed closure consists of two…
Interpretable reduced-order modeling with time-scale separation
Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis, Petros Koumoutsakos
Partial Differential Equations (PDEs) with high dimensionality are commonly encountered in computational physics and engineering. However, finding solutions for these PDEs can be c…
Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems
Sebastian Kaltenbach, Paris Perdikaris, Phaedon-Stelios Koutsourelakis
Neural Operators offer a powerful, data-driven tool for solving parametric PDEs as they can represent maps between infinite-dimensional function spaces. In this work, we employ phy…
Physics-enhanced Neural Networks in the Small Data Regime
Jonas Eichelsdörfer, Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
Identifying the dynamics of physical systems requires a machine learning model that can assimilate observational data, but also incorporate the laws of physics. Neural Networks bas…
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…
Physics-aware, deep probabilistic modeling of multiscale dynamics in the Small Data regime
Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
The data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the…
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…
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-…