Phaedon-Stelios Koutsourelakis
Orcid: 0000-0002-9345-759XAffiliations:
- Technical University of Munich, Germany
According to our database1,
Phaedon-Stelios Koutsourelakis
authored at least 30 papers
between 2007 and 2024.
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Bibliography
2024
A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty.
J. Comput. Phys., 2024
Embedded Model Bias Quantification with Measurement Noise for Bayesian Model Calibration.
CoRR, 2024
Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography.
CoRR, 2024
Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media.
CoRR, 2024
2023
Model bias identification for Bayesian calibration of stochastic digital twins of bridges.
CoRR, 2023
CoRR, 2023
2022
2021
A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables.
J. Comput. Phys., 2021
Self-supervised optimization of random material microstructures in the small-data regime.
CoRR, 2021
Proceedings of the 9th International Conference on Learning Representations, 2021
2020
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems.
J. Comput. Phys., 2020
Embedded-physics machine learning for coarse-graining and collective variable discovery without data.
CoRR, 2020
A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Propagation in Complex Physical Simulations.
CoRR, 2020
2019
Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media.
SIAM/ASA J. Uncertain. Quantification, 2019
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data.
J. Comput. Phys., 2019
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime.
J. Comput. Phys., 2019
2018
A data-driven model order reduction approach for Stokes flow through random porous media.
CoRR, 2018
2017
Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics.
J. Comput. Phys., 2017
2016
J. Comput. Phys., 2016
J. Comput. Phys., 2016
2012
Free energy computations by minimization of Kullback-Leibler divergence: An efficient adaptive biasing potential method for sparse representations.
J. Comput. Phys., 2012
2011
Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems.
Multiscale Model. Simul., 2011
2009
SIAM J. Sci. Comput., 2009
A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters.
J. Comput. Phys., 2009
2008
Proceedings of the Social Information Processing, 2008
2007
J. Comput. Phys., 2007