Phaedon-Stelios Koutsourelakis

42 papers A* 1Journal 40Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	GenPANIS: A Latent-Variable Generative Framework for Forward and Inverse PDE Problems in Multiphase Media. CoRR Matthaios Chatzopoulos, Phaedon-Stelios Koutsourelakis
2025	J	`jnl`	DGNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling. CoRR Yaohua Zang, Phaedon-Stelios Koutsourelakis
2025	J	`jnl`	DGenNO: a novel physics-aware neural operator for solving forward and inverse PDE problems based on deep, generative probabilistic modeling. J. Comput. Phys. Yaohua Zang, Phaedon-Stelios Koutsourelakis
2025	J	`jnl`	Efficient Bayesian multi-fidelity inverse analysis for expensive and non-differentiable physics-based simulations in high stochastic dimensions. CoRR Jonas Nitzler, Bugrahan Z. Temür, Phaedon-Stelios Koutsourelakis, Wolfgang A. Wall
2025	J	`jnl`	Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework Without Data. CoRR Maximilian Stupp, Phaedon-Stelios Koutsourelakis
2025	J	`jnl`	Quantification of model error for inverse problems in the Weak Neural Variational Inference framework. CoRR Vincent C. Scholz, Phaedon-Stelios Koutsourelakis
2025	J	`jnl`	Stochastic black-box optimization using multi-fidelity score function estimator. Mach. Learn. Sci. Technol. Atul Agrawal, Kislaya Ravi, Phaedon-Stelios Koutsourelakis, Hans-Joachim Bungartz
2024	J	`jnl`	A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty. J. Comput. Phys. Atul Agrawal, Phaedon-Stelios Koutsourelakis
2024	J	`jnl`	Embedded Model Bias Quantification with Measurement Noise for Bayesian Model Calibration. CoRR Daniel Andrés Arcones, Martin Weiser, Phaedon-Stelios Koutsourelakis, Jörg F. Unger
2024	J	`jnl`	Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media. CoRR Matthaios Chatzopoulos, Phaedon-Stelios Koutsourelakis
2024	J	`jnl`	Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography. CoRR Vincent C. Scholz, Yaohua Zang, Phaedon-Stelios Koutsourelakis
2023	J	`jnl`	A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty. CoRR Atul Agrawal, Phaedon-Stelios Koutsourelakis
2023	J	`jnl`	Interpretable reduced-order modeling with time-scale separation. CoRR Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis, Petros Koumoutsakos
2023	J	`jnl`	Model bias identification for Bayesian calibration of stochastic digital twins of bridges. CoRR Daniel Andrés Arcones, Martin Weiser, Phaedon-Stelios Koutsourelakis, Jörg F. Unger
2023	J	`jnl`	Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators. CoRR Atul Agrawal, Kislaya Ravi, Phaedon-Stelios Koutsourelakis, Hans-Joachim Bungartz
2022	J	`jnl`	Semi-supervised Invertible DeepONets for Bayesian Inverse Problems. CoRR Sebastian Kaltenbach, Paris Perdikaris, Phaedon-Stelios Koutsourelakis
2021	J	`jnl`	A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables. J. Comput. Phys. Maximilian Rixner, Phaedon-Stelios Koutsourelakis
2021	A*	`conf`	Physics-aware, probabilistic model order reduction with guaranteed stability. ICLR Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
2021	J	`jnl`	Physics-aware, probabilistic model order reduction with guaranteed stability. CoRR Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
2021	J	`jnl`	Physics-enhanced Neural Networks in the Small Data Regime. CoRR Jonas Eichelsdörfer, Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
2021	J	`jnl`	Self-supervised optimization of random material microstructures in the small-data regime. CoRR Maximilian Rixner, Phaedon-Stelios Koutsourelakis
2020	J	`jnl`	A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Propagation in Complex Physical Simulations. CoRR Jonas Nitzler, Jonas Biehler, Niklas Fehn, Phaedon-Stelios Koutsourelakis, Wolfgang A. Wall
2020	J	`jnl`	A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables. CoRR Maximilian Rixner, Phaedon-Stelios Koutsourelakis
2020	J	`jnl`	Embedded-physics machine learning for coarse-graining and collective variable discovery without data. CoRR Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
2020	J	`jnl`	Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems. J. Comput. Phys. Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
2019	J	`jnl`	A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime. CoRR Constantin Grigo, Phaedon-Stelios Koutsourelakis
2019	J	`jnl`	A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime. J. Comput. Phys. Constantin Grigo, Phaedon-Stelios Koutsourelakis
2019	J	`jnl`	Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media. SIAM/ASA J. Uncertain. Quantification Constantin Grigo, Phaedon-Stelios Koutsourelakis
2019	J	`jnl`	Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data. CoRR Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
2019	J	`jnl`	Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. J. Comput. Phys. Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
2018	J	`jnl`	A data-driven model order reduction approach for Stokes flow through random porous media. CoRR Constantin Grigo, Phaedon-Stelios Koutsourelakis
2018	J	`jnl`	Predictive Collective Variable Discovery with Deep Bayesian Models. CoRR Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
2017	J	`jnl`	Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics. J. Comput. Phys. Franck Monmont, Phaedon-Stelios Koutsourelakis
2017	J	`jnl`	Predictive coarse-graining. J. Comput. Phys. Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
2016	J	`jnl`	Special Issue: Big data and predictive computational modeling. J. Comput. Phys. Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, Michele Girolami
2016	J	`jnl`	Variational Bayesian strategies for high-dimensional, stochastic design problems. J. Comput. Phys. Phaedon-Stelios Koutsourelakis
2012	J	`jnl`	Free energy computations by minimization of Kullback-Leibler divergence: An efficient adaptive biasing potential method for sparse representations. J. Comput. Phys. Ilias Bilionis, Phaedon-Stelios Koutsourelakis
2011	J	`jnl`	Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems. Multiscale Model. Simul. Phaedon-Stelios Koutsourelakis, Elias Bilionis
2009	J	`jnl`	A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters. J. Comput. Phys. Phaedon-Stelios Koutsourelakis
2009	J	`jnl`	Accurate Uncertainty Quantification Using Inaccurate Computational Models. SIAM J. Sci. Comput. Phaedon-Stelios Koutsourelakis
2008	—	`conf`	Finding Mixed-Memberships in Social Networks. AAAI Spring Symposium: Social Information Processing Phaedon-Stelios Koutsourelakis, Tina Eliassi-Rad
2007	J	`jnl`	Stochastic upscaling in solid mechanics: An excercise in machine learning. J. Comput. Phys. Phaedon-Stelios Koutsourelakis