Stochastic upscaling in solid mechanics: An excercise in machine learning. - Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2007)

Finding Mixed-Memberships in Social Networks. - Phaedon-Stelios Koutsourelakis; Tina Eliassi-Rad (AAAI Spring Symposium: Social Information Processing, 2008)

A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters. - Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2009)

Accurate Uncertainty Quantification Using Inaccurate Computational Models. - Phaedon-Stelios Koutsourelakis (SIAM J. Sci. Comput., 2009)

Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems. - Phaedon-Stelios Koutsourelakis; Elias Bilionis (Multiscale Model. Simul., 2011)

Free energy computations by minimization of Kullback-Leibler divergence: An efficient adaptive biasing potential method for sparse representations. - Ilias Bilionis; Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2012)

Variational Bayesian strategies for high-dimensional, stochastic design problems. - Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2016)

Special Issue: Big data and predictive computational modeling. - Phaedon-Stelios Koutsourelakis; Nicholas Zabaras; Michele Girolami (J. Comput. Phys., 2016)

Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics. - Franck Monmont; Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2017)

Predictive coarse-graining. - Markus Schöberl; Nicholas Zabaras; Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2017)

A data-driven model order reduction approach for Stokes flow through random porous media. - Constantin Grigo; Phaedon-Stelios Koutsourelakis (CoRR, 2018)

Predictive Collective Variable Discovery with Deep Bayesian Models. - Markus Schöberl; Nicholas Zabaras; Phaedon-Stelios Koutsourelakis (CoRR, 2018)

A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime. - Constantin Grigo; Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2019)

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. - Yinhao Zhu; Nicholas Zabaras; Phaedon-Stelios Koutsourelakis; Paris Perdikaris (J. Comput. Phys., 2019)

Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media. - Constantin Grigo; Phaedon-Stelios Koutsourelakis (SIAM/ASA J. Uncertain. Quantification, 2019)

A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Propagation in Complex Physical Simulations. - Jonas Nitzler; Jonas Biehler; Niklas Fehn; Phaedon-Stelios Koutsourelakis; Wolfgang A. Wall (CoRR, 2020)

Embedded-physics machine learning for coarse-graining and collective variable discovery without data. - Markus Schöberl; Nicholas Zabaras; Phaedon-Stelios Koutsourelakis (CoRR, 2020)

Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems. - Sebastian Kaltenbach; Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2020)

Physics-aware, probabilistic model order reduction with guaranteed stability. - Sebastian Kaltenbach; Phaedon-Stelios Koutsourelakis (ICLR, 2021)

Self-supervised optimization of random material microstructures in the small-data regime. - Maximilian Rixner; Phaedon-Stelios Koutsourelakis (CoRR, 2021)

Physics-enhanced Neural Networks in the Small Data Regime. - Jonas Eichelsdörfer; Sebastian Kaltenbach; Phaedon-Stelios Koutsourelakis (CoRR, 2021)

A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables. - Maximilian Rixner; Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2021)

Semi-supervised Invertible DeepONets for Bayesian Inverse Problems. - Sebastian Kaltenbach; Paris Perdikaris; Phaedon-Stelios Koutsourelakis (CoRR, 2022)

Interpretable reduced-order modeling with time-scale separation. - Sebastian Kaltenbach; Phaedon-Stelios Koutsourelakis; Petros Koumoutsakos (CoRR, 2023)

Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators. - Atul Agrawal; Kislaya Ravi; Phaedon-Stelios Koutsourelakis; Hans-Joachim Bungartz (CoRR, 2023)

Model bias identification for Bayesian calibration of stochastic digital twins of bridges. - Daniel Andrés Arcones; Martin Weiser; Phaedon-Stelios Koutsourelakis; Jörg F. Unger (CoRR, 2023)

Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media. - Matthaios Chatzopoulos; Phaedon-Stelios Koutsourelakis (CoRR, 2024)

Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography. - Vincent C. Scholz; Yaohua Zang; Phaedon-Stelios Koutsourelakis (CoRR, 2024)

Embedded Model Bias Quantification with Measurement Noise for Bayesian Model Calibration. - Daniel Andrés Arcones; Martin Weiser; Phaedon-Stelios Koutsourelakis; Jörg F. Unger (CoRR, 2024)

A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty. - Atul Agrawal; Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2024)

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 (J. Comput. Phys., 2025)

Stochastic black-box optimization using multi-fidelity score function estimator. - Atul Agrawal; Kislaya Ravi; Phaedon-Stelios Koutsourelakis; Hans-Joachim Bungartz (Mach. Learn. Sci. Technol., 2025)

DGNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling. - Yaohua Zang; Phaedon-Stelios Koutsourelakis (CoRR, 2025)

Quantification of model error for inverse problems in the Weak Neural Variational Inference framework. - Vincent C. Scholz; Phaedon-Stelios Koutsourelakis (CoRR, 2025)

Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework Without Data. - Maximilian Stupp; Phaedon-Stelios Koutsourelakis (CoRR, 2025)

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; Wolfgang A. Wall (CoRR, 2025)