Phaedon-Stelios Koutsourelakis

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 28

Top Topics

Inverse Problems

Machine Learning

Computational Modeling

Top Venues

J. Comput. Phys.

SIAM/ASA J. Uncertain. Quantification

Publications

Atul Agrawal, Phaedon-Stelios Koutsourelakis
A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty. J. Comput. Phys. 508 (2024)
Vincent C. Scholz, Yaohua Zang, Phaedon-Stelios Koutsourelakis
Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography. CoRR (2024)
Matthaios Chatzopoulos, Phaedon-Stelios Koutsourelakis
Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media. CoRR (2024)
Atul Agrawal, Kislaya Ravi, Phaedon-Stelios Koutsourelakis, Hans-Joachim Bungartz
Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators. CoRR (2023)
Daniel Andrés Arcones, Martin Weiser, Phaedon-Stelios Koutsourelakis, Jörg F. Unger
Model bias identification for Bayesian calibration of stochastic digital twins of bridges. CoRR (2023)
Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis, Petros Koumoutsakos
Interpretable reduced-order modeling with time-scale separation. CoRR (2023)
Atul Agrawal, Phaedon-Stelios Koutsourelakis
A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty. CoRR (2023)
Sebastian Kaltenbach, Paris Perdikaris, Phaedon-Stelios Koutsourelakis
Semi-supervised Invertible DeepONets for Bayesian Inverse Problems. CoRR (2022)
Maximilian Rixner, Phaedon-Stelios Koutsourelakis
Self-supervised optimization of random material microstructures in the small-data regime. CoRR (2021)
Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
Physics-aware, probabilistic model order reduction with guaranteed stability. ICLR (2021)
Jonas Eichelsdörfer, Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
Physics-enhanced Neural Networks in the Small Data Regime. CoRR (2021)
Maximilian Rixner, Phaedon-Stelios Koutsourelakis
A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables. J. Comput. Phys. 434 (2021)
Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
Physics-aware, probabilistic model order reduction with guaranteed stability. CoRR (2021)
Jonas Nitzler, Jonas Biehler, Niklas Fehn, Phaedon-Stelios Koutsourelakis, Wolfgang A. Wall
A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Propagation in Complex Physical Simulations. CoRR (2020)
Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems. J. Comput. Phys. 419 (2020)
Maximilian Rixner, Phaedon-Stelios Koutsourelakis
A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables. CoRR (2020)
Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
Embedded-physics machine learning for coarse-graining and collective variable discovery without data. CoRR (2020)
Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. J. Comput. Phys. 394 (2019)
Constantin Grigo, Phaedon-Stelios Koutsourelakis
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime. J. Comput. Phys. 397 (2019)
Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data. CoRR (2019)
Constantin Grigo, Phaedon-Stelios Koutsourelakis
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime. CoRR (2019)
Constantin Grigo, Phaedon-Stelios Koutsourelakis
Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media. SIAM/ASA J. Uncertain. Quantification 7 (1) (2019)
Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
Predictive Collective Variable Discovery with Deep Bayesian Models. CoRR (2018)
Constantin Grigo, Phaedon-Stelios Koutsourelakis
A data-driven model order reduction approach for Stokes flow through random porous media. CoRR (2018)
Franck Monmont, Phaedon-Stelios Koutsourelakis
Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics. J. Comput. Phys. 329 (2017)
Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
Predictive coarse-graining. J. Comput. Phys. 333 (2017)
Phaedon-Stelios Koutsourelakis
Variational Bayesian strategies for high-dimensional, stochastic design problems. J. Comput. Phys. 308 (2016)
Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, Michele Girolami
Special Issue: Big data and predictive computational modeling. J. Comput. Phys. 321 (2016)
Ilias Bilionis, Phaedon-Stelios Koutsourelakis
Free energy computations by minimization of Kullback-Leibler divergence: An efficient adaptive biasing potential method for sparse representations. J. Comput. Phys. 231 (9) (2012)
Phaedon-Stelios Koutsourelakis, Elias Bilionis
Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems. Multiscale Model. Simul. 9 (1) (2011)
Phaedon-Stelios Koutsourelakis
Accurate Uncertainty Quantification Using Inaccurate Computational Models. SIAM J. Sci. Comput. 31 (5) (2009)
Phaedon-Stelios Koutsourelakis
A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters. J. Comput. Phys. 228 (17) (2009)
Phaedon-Stelios Koutsourelakis, Tina Eliassi-Rad
Finding Mixed-Memberships in Social Networks. AAAI Spring Symposium: Social Information Processing (2008)
Phaedon-Stelios Koutsourelakis
Stochastic upscaling in solid mechanics: An excercise in machine learning. J. Comput. Phys. 226 (1) (2007)