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ORCIDPhaedon-Stelios Koutsourelakis
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- affiliation: Technical University of Munich, Germany
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Journal Articles
- 2024
[j15]Atul Agrawal
, Phaedon-Stelios Koutsourelakis:
A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty. J. Comput. Phys. 508: 112982 (2024)- 2021
- [j14]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: 110218 (2021) - 2020
- [j13]Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis:
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems. J. Comput. Phys. 419: 109673 (2020) - 2019
- [j12]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: 56-81 (2019) - [j11]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) - [j10]Constantin Grigo, Phaedon-Stelios Koutsourelakis:
Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media. SIAM/ASA J. Uncertain. Quantification 7(1): 292-323 (2019) - 2017
- [j9]Franck Monmont, Phaedon-Stelios Koutsourelakis:
Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics. J. Comput. Phys. 329: 91-125 (2017) - [j8]Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis:
Predictive coarse-graining. J. Comput. Phys. 333: 49-77 (2017) - 2016
- [j7]Phaedon-Stelios Koutsourelakis:
Variational Bayesian strategies for high-dimensional, stochastic design problems. J. Comput. Phys. 308: 124-152 (2016) - [j6]Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, Michele Girolami:
Special Issue: Big data and predictive computational modeling. J. Comput. Phys. 321: 1252-1254 (2016) - 2012
- [j5]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): 3849-3870 (2012) - 2011
- [j4]Phaedon-Stelios Koutsourelakis, Elias Bilionis:
Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems. Multiscale Model. Simul. 9(1): 449-485 (2011) - 2009
- [j3]Phaedon-Stelios Koutsourelakis:
A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters. J. Comput. Phys. 228(17): 6184-6211 (2009) - [j2]Phaedon-Stelios Koutsourelakis:
Accurate Uncertainty Quantification Using Inaccurate Computational Models. SIAM J. Sci. Comput. 31(5): 3274-3300 (2009) - 2007
- [j1]Phaedon-Stelios Koutsourelakis:
Stochastic upscaling in solid mechanics: An excercise in machine learning. J. Comput. Phys. 226(1): 301-325 (2007)
Conference and Workshop Papers
- 2021
- [c2]Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis:
Physics-aware, probabilistic model order reduction with guaranteed stability. ICLR 2021 - 2008
- [c1]Phaedon-Stelios Koutsourelakis, Tina Eliassi-Rad:
Finding Mixed-Memberships in Social Networks. AAAI Spring Symposium: Social Information Processing 2008: 48-53
Informal and Other Publications
- 2024
- [i17]Matthaios Chatzopoulos, Phaedon-Stelios Koutsourelakis:
Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media. CoRR abs/2405.19019 (2024) - [i16]Vincent C. Scholz, Yaohua Zang, Phaedon-Stelios Koutsourelakis:
Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography. CoRR abs/2407.20697 (2024) - 2023
- [i15]Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis, Petros Koumoutsakos:
Interpretable reduced-order modeling with time-scale separation. CoRR abs/2303.02189 (2023) - [i14]Atul Agrawal, Phaedon-Stelios Koutsourelakis:
A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty. CoRR abs/2307.02432 (2023) - [i13]Atul Agrawal, Kislaya Ravi, Phaedon-Stelios Koutsourelakis, Hans-Joachim Bungartz:
Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators. CoRR abs/2311.15137 (2023) - [i12]Daniel Andrés Arcones, Martin Weiser, Phaedon-Stelios Koutsourelakis, Jörg F. Unger:
Model bias identification for Bayesian calibration of stochastic digital twins of bridges. CoRR abs/2312.00664 (2023) - 2022
- [i11]Sebastian Kaltenbach, Paris Perdikaris, Phaedon-Stelios Koutsourelakis:
Semi-supervised Invertible DeepONets for Bayesian Inverse Problems. CoRR abs/2209.02772 (2022) - 2021
- [i10]Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis:
Physics-aware, probabilistic model order reduction with guaranteed stability. CoRR abs/2101.05834 (2021) - [i9]Maximilian Rixner, Phaedon-Stelios Koutsourelakis:
Self-supervised optimization of random material microstructures in the small-data regime. CoRR abs/2108.02606 (2021) - [i8]Jonas Eichelsdörfer, Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis:
Physics-enhanced Neural Networks in the Small Data Regime. CoRR abs/2111.10329 (2021) - 2020
- [i7]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 abs/2001.02892 (2020) - [i6]Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis:
Embedded-physics machine learning for coarse-graining and collective variable discovery without data. CoRR abs/2002.10148 (2020) - [i5]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 abs/2006.01789 (2020) - 2019
- [i4]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 abs/1901.06314 (2019) - [i3]Constantin Grigo, Phaedon-Stelios Koutsourelakis:
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime. CoRR abs/1902.03968 (2019) - 2018
- [i2]Constantin Grigo, Phaedon-Stelios Koutsourelakis:
A data-driven model order reduction approach for Stokes flow through random porous media. CoRR abs/1806.08117 (2018) - [i1]Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis:
Predictive Collective Variable Discovery with Deep Bayesian Models. CoRR abs/1809.06913 (2018)
Coauthor Index
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