Phaedon‐Stelios Koutsourelakis

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All published works

Action	Title	Year	Authors
+ PDF Chat	Quantification of model error for inverse problems in the Weak Neural Variational Inference framework	2025	Vincent C. Scholz Phaedon‐Stelios Koutsourelakis
+	Weak neural variational inference for solving Bayesian inverse problems without forward models: Applications in elastography	2024	Vincent C. Scholz Yaohua Zang Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Embedded Model Bias Quantification with Measurement Noise for Bayesian Model Calibration	2024	Daniel Andrés Arcones Martin Weiser Phaedon‐Stelios Koutsourelakis Jörg F. Unger
+	Model Bias Identification for Bayesian Calibration of Stochastic Digital Twins of Bridges	2024	Daniel Andrés Arcones Martin Weiser Phaedon‐Stelios Koutsourelakis Jörg F. Unger
+ PDF Chat	PSP-GEN: Stochastic inversion of the Process-Structure-Property chain in materials design through deep, generative probabilistic modeling	2024	Yaohua Zang Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography	2024	Vincent C. Scholz Yaohua Zang Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media	2024	Matthaios Chatzopoulos Phaedon‐Stelios Koutsourelakis
+	A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty	2024	Atul Agrawal Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Physics-Aware Neural Implicit Solvers for Multiscale, Parametric Pdes with Applications in Heterogeneous Media	2024	Matthaios Chatzopoulos Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Weak Neural Variational Inference for Solving Bayesian Inverse Problems Without Forward Models: Applications in Elastography	2024	Vincent C. Scholz Yaohua Zang Phaedon‐Stelios Koutsourelakis
+	From concrete mixture to structural design—a holistic optimization procedure in the presence of uncertainties	2024	Atul Agrawal Erik Tamsen Jörg F. Unger Phaedon‐Stelios Koutsourelakis
+	Interpretable reduced-order modeling with time-scale separation	2023	Sebastian Kaltenbach Phaedon‐Stelios Koutsourelakis Petros Koumoutsakos
+	A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty	2023	Atul Agrawal Phaedon‐Stelios Koutsourelakis
+	Physics-Informed Tensor Basis Neural Network for Turbulence Closure Modeling	2023	Leon Riccius Atul Agrawal Phaedon‐Stelios Koutsourelakis
+	Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators	2023	Atul Agrawal Kislaya Ravi Phaedon‐Stelios Koutsourelakis Hans–Joachim Bungartz
+	Model bias identification for Bayesian calibration of stochastic digital twins of bridges	2023	Daniel Andrés Arcones Martin Weiser Phaedon‐Stelios Koutsourelakis Jörg F. Unger
+	From concrete mixture to structural design -- a holistic optimization procedure in the presence of uncertainties	2023	Atul Agrawal Erik Tamsen Phaedon‐Stelios Koutsourelakis J. Unger
+	Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems	2022	Sebastian Kaltenbach Paris Perdikaris Phaedon‐Stelios Koutsourelakis
+	Physics-enhanced Neural Networks in the Small Data Regime	2021	Jonas Eichelsdörfer Sebastian Kaltenbach Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Physics-enhanced Neural Networks in the Small Data Regime	2021	Jonas Eichelsdörfer Sebastian Kaltenbach Phaedon‐Stelios Koutsourelakis
+ PDF Chat	A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables	2021	Maximilian Rixner Phaedon‐Stelios Koutsourelakis
+	Physics-aware, deep probabilistic modeling of multiscale dynamics in the Small Data regime.	2021	Sebastian Kaltenbach Phaedon‐Stelios Koutsourelakis
+	Physics-aware, probabilistic model order reduction with guaranteed stability	2021	Sebastian Kaltenbach Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Physics-Aware, Deep Probabilistic Modeling of Multiscale Dynamics in the Small Data Regime	2021	Sebastian Kaltenbach Phaedon‐Stelios Koutsourelakis
+	Self-supervised optimization of random material microstructures in the small-data regime	2021	Maximilian Rixner Phaedon‐Stelios Koutsourelakis
+	Physics-aware, deep probabilistic modeling of multiscale dynamics in the Small Data regime	2021	Sebastian Kaltenbach Phaedon‐Stelios Koutsourelakis
+	Physics-enhanced Neural Networks in the Small Data Regime	2021	Jonas Eichelsdörfer Sebastian Kaltenbach Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems	2020	Sebastian Kaltenbach Phaedon‐Stelios Koutsourelakis
+	A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Propagation in Complex Physical Simulations.	2020	Jonas Nitzler Jonas Biehler Niklas Fehn Phaedon‐Stelios Koutsourelakis Wolfgang A. Wall
+	Embedded-physics machine learning for coarse-graining and collective variable discovery without data	2020	Markus Schöberl Nicholas Zabaras Phaedon‐Stelios Koutsourelakis
+	A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Propagation in Complex Physical Simulations	2020	Jonas Nitzler Jonas Biehler Niklas Fehn Phaedon‐Stelios Koutsourelakis Wolfgang A. Wall
+ PDF Chat	A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime	2019	Constantin Grigo Phaedon‐Stelios Koutsourelakis
+	Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data	2019	Yinhao Zhu Nicholas Zabaras Phaedon‐Stelios Koutsourelakis Paris Perdikaris
+ PDF Chat	Predictive collective variable discovery with deep Bayesian models	2019	Markus Schöberl Nicholas Zabaras Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media	2019	Constantin Grigo Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Physics-Constrained, Data-Driven Discovery of Coarse-Grained Dynamics	2019	Lukas Felsberger Phaedon‐Stelios Koutsourelakis
+ PDF Chat	A data‐driven model order reduction approach for Stokes flow through random porous media	2018	Constantin Grigo Phaedon‐Stelios Koutsourelakis
+	Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography	2018	Lukas Bruder Phaedon‐Stelios Koutsourelakis
+	Physics-constrained, data-driven discovery of coarse-grained dynamics	2018	Lukas Felsberger Phaedon‐Stelios Koutsourelakis
+ PDF Chat	BEYOND BLACK-BOXES IN BAYESIAN INVERSE PROBLEMS AND MODEL VALIDATION: APPLICATIONS IN SOLID MECHANICS OF ELASTOGRAPHY	2018	Lukas Bruder Phaedon‐Stelios Koutsourelakis
+	Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography	2018	Lukas Bruder Phaedon‐Stelios Koutsourelakis
+	PROBABILISTIC REDUCED-ORDER MODELING FOR STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS	2017	Constantin Grigo Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Predictive coarse-graining	2016	Markus Schöberl Nicholas Zabaras Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics	2016	Isabell M. Franck Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Variational Bayesian strategies for high-dimensional, stochastic design problems	2015	Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Sparse Variational Bayesian approximations for nonlinear inverse problems: Applications in nonlinear elastography	2015	Isabell M. Franck Phaedon‐Stelios Koutsourelakis
+	Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics	2015	Isabell M. Franck Phaedon‐Stelios Koutsourelakis
+	Variational Bayesian Formulations for High-Dimensional Inverse Problems	2015	Isabell M. Franck Phaedon‐Stelios Koutsourelakis
+	Sparse Variational Bayesian Approximations for Nonlinear Inverse Problems: applications in nonlinear elastography	2014	Isabell M. Franck Phaedon‐Stelios Koutsourelakis
+	Simulation-based, high-dimensional stochastic optimization: application in robust topology optimization under large material uncertainties	2014	Phaedon‐Stelios Koutsourelakis
+ PDF Chat	A novel Bayesian strategy for the identification of spatially varying material properties and model validation: an application to static elastography	2012	Phaedon‐Stelios Koutsourelakis
+ PDF Chat	Free energy computations by minimization of Kullback–Leibler divergence: An efficient adaptive biasing potential method for sparse representations	2012	Ilias Bilionis Phaedon‐Stelios Koutsourelakis
+	Bayesian reduced-order models for multiscale dynamical systems	2010	Phaedon‐Stelios Koutsourelakis Elias Bilionis
+	Scalable Bayesian reduced-order models for high-dimensional multiscale dynamical systems	2010	Phaedon‐Stelios Koutsourelakis Elias Bilionis
+	Free energy computations by minimization of Kullback-Leibler divergence: an efficient adaptive biasing potential method for sparse representations	2010	Ilias Bilionis Phaedon‐Stelios Koutsourelakis
+ PDF Chat	A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters	2009	Phaedon‐Stelios Koutsourelakis
+	Uncertainty quantification in complex systems using approximate solvers	2008	Phaedon‐Stelios Koutsourelakis
+	Probabilistic characterization and simulation of multi-phase random media	2005	Phaedon‐Stelios Koutsourelakis
+	A critical appraisal of reliability estimation procedures for high dimensions	2004	G.I. Schuëller H.J. Pradlwarter Phaedon‐Stelios Koutsourelakis
+	Reliability of structures in high dimensions. Part II. Theoretical validation	2004	Phaedon‐Stelios Koutsourelakis

Common Coauthors

Coauthor	Papers Together
Sebastian Kaltenbach	10
Atul Agrawal	6
Isabell M. Franck	5
Constantin Grigo	4
Yaohua Zang	4
Vincent C. Scholz	4
Jörg F. Unger	4
Nicholas Zabaras	4
Jonas Eichelsdörfer	3
Daniel Andrés Arcones	3
Markus Schöberl	3
Jonas Biehler	2
Paris Perdikaris	2
Elias Bilionis	2
Erik Tamsen	2
Maximilian Rixner	2
Matthaios Chatzopoulos	2
Martin Weiser	2
Jonas Nitzler	2
Ilias Bilionis	2
Lukas Felsberger	2
Niklas Fehn	2
Wolfgang A. Wall	2
Lukas Bruder	2
Kislaya Ravi	1
Lukas Bruder	1
H.J. Pradlwarter	1
Yinhao Zhu	1
G.I. Schuëller	1
Hans‐Joachim Bungartz	1
Petros Koumoutsakos	1
Leon Riccius	1
J. Unger	1
Martin Weiser	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants	1998	Radford M. Neal Geoffrey E. Hinton	11
+	Stochastic Variational Inference	2012	Matt Hoffman David M. Blei Chong Wang John Paisley	10
+	Pattern Recognition and Machine Learning	2007	Christopher Bishop	10
+	Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm	1977	A. P. Dempster N. M. Laird Donald B. Rubin	9
+ PDF Chat	Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification	2018	Yinhao Zhu Nicholas Zabaras	8
+ PDF Chat	Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems	2020	Sebastian Kaltenbach Phaedon‐Stelios Koutsourelakis	8
+	Variational Relevance Vector Machines	2013	Chris Bishop Michael E. Tipping	7
+	Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data	2019	Yinhao Zhu Nicholas Zabaras Phaedon‐Stelios Koutsourelakis Paris Perdikaris	7
+	Stochastic variational inference	2013	Matthew D. Hoffman David M. Blei Chong Wang John Paisley	7
+ PDF Chat	Monte Carlo Statistical Methods	2000	Hoon Kim Christian P. Robert George Casella	7
+ PDF Chat	Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media	2019	Constantin Grigo Phaedon‐Stelios Koutsourelakis	6
+ PDF Chat	Sequential Monte Carlo Samplers	2006	Pierre Del Moral Arnaud Doucet Ajay Jasra	6
+ PDF Chat	Predictive coarse-graining	2016	Markus Schöberl Nicholas Zabaras Phaedon‐Stelios Koutsourelakis	6
+	Solution of inverse problems with limited forward solver evaluations: a Bayesian perspective	2013	Ilias Bilionis Nicholas Zabaras	6
+	Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media	2018	Shaoxing Mo Yinhao Zhu Nicholas Zabaras Xiaoqing Shi Jichun Wu	6
+ PDF Chat	A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters	2009	Phaedon‐Stelios Koutsourelakis	6
+	Discovering governing equations from data by sparse identification of nonlinear dynamical systems	2016	Steven L. Brunton Joshua L. Proctor J. Nathan Kutz	5
+ PDF Chat	Gaussian Markov random field priors for inverse problems	2013	Johnathan M. Bardsley	5
+	Adjoint‐weighted variational formulation for the direct solution of inverse problems of general linear elasticity with full interior data	2009	Paul E. Barbone Carlos E. Rivas Isaac Harari Uri Albocher Assad A. Oberai Yixiao Zhang	5
+ PDF Chat	Inducing features of random fields	1997	S. Della Pietra V. Della Pietra John Lafferty	5
+ PDF Chat	Physics-Constrained, Data-Driven Discovery of Coarse-Grained Dynamics	2019	Lukas Felsberger Phaedon‐Stelios Koutsourelakis	5
+	Variational Bayesian Inference with Stochastic Search	2012	John Paisley David M. Blei Michael I. Jordan	5
+	Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations	2017	Maziar Raissi Paris Perdikaris George Em Karniadakis	5
+ PDF Chat	Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference	2004	Nicolás Chopin	5
+	Hidden physics models: Machine learning of nonlinear partial differential equations	2017	Maziar Raissi George Em Karniadakis	5
+ PDF Chat	A feasible method for optimization with orthogonality constraints	2012	Zaiwen Wen Wotao Yin	5
+	Hierarchical Bayesian models for inverse problems in heat conduction	2004	Jingbo Wang Nicholas Zabaras	5
+	Reduced Basis Methods for Partial Differential Equations: An Introduction	2015	Alfio Quarteroni Andrea Manzoni Federico Negri	4
+ PDF Chat	Simulating normalizing constants: from importance sampling to bridge sampling to path sampling	1998	Andrew Gelman Xiao‐Li Meng	4
+	The information bottleneck method	2000	Naftali Tishby Fernando C. N. Pereira William Bialek	4
+ PDF Chat	Extracting macroscopic dynamics: model problems and algorithms	2004	Dror Givon Raz Kupferman Andrew M. Stuart	4
+	Escaping free-energy minima	2002	Alessandro Laio Michele Parrinello	4
+ PDF Chat	Bayesian computation: a summary of the current state, and samples backwards and forwards	2015	Peter J. Green Krzysztof Łatuszyński Marcelo Pereyra Christian P. Robert	4
+ PDF Chat	Certified Reduced Basis Methods for Parametrized Partial Differential Equations	2015	Jan S. Hesthaven Gianluigi Rozza Benjamin Stamm	4
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	4
+ PDF Chat	A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables	2021	Maximilian Rixner Phaedon‐Stelios Koutsourelakis	4
+ PDF Chat	Free energy methods for Bayesian inference: efficient exploration of univariate Gaussian mixture posteriors	2011	Nicolás Chopin Tony Lelièvre Gabriel Stoltz	4
+ PDF Chat	Convergence of a stochastic approximation version of the EM algorithm	1999	Bernard Delyon Marc Lavielle Éric Moulines	4
+	High-Order Collocation Methods for Differential Equations with Random Inputs	2005	Dongbin Xiu Jan S. Hesthaven	4
+ PDF Chat	Sparse Variational Bayesian approximations for nonlinear inverse problems: Applications in nonlinear elastography	2015	Isabell M. Franck Phaedon‐Stelios Koutsourelakis	4
+ PDF Chat	A novel Bayesian strategy for the identification of spatially varying material properties and model validation: an application to static elastography	2012	Phaedon‐Stelios Koutsourelakis	4
+ PDF Chat	A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime	2019	Constantin Grigo Phaedon‐Stelios Koutsourelakis	4
+	Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems	2019	Yibo Yang Paris Perdikaris	4
+ PDF Chat	A variational Bayesian method to inverse problems with impulsive noise	2011	Bangti Jin	4
+	Variational Bayesian Inference for a Nonlinear Forward Model	2008	Michael A. Chappell Adrian R. Groves Brandon Whitcher Mark W. Woolrich	3
+	The FEniCS Project Version 1.5	2015	Martin Sandve Alnæs Jan Blechta Johan Hake August Johansson Benjamin Kehlet Anders Logg Chris Richardson Johannes Ring Marie E. Rognes Garth N. Wells	3
+	Extreme-scale UQ for Bayesian inverse problems governed by PDEs	2012	Tan Bui‐Thanh Carsten Burstedde Omar Ghattas James L. Martin Georg Stadler Lucas C. Wilcox	3
+	Equation-Free Multiscale Computation: enabling microscopic simulators to perform system-level tasks	2002	Ioannis G. Kevrekidis C. W. Gear James M. Hyman Panagiotis Kevrekidis Olof Runborg Constantinos Theodoropoulos	3
+	When Is a Model Good Enough? Deriving the Expected Value of Model Improvement via Specifying Internal Model Discrepancies	2014	Mark Strong Jeremy E. Oakley	3
+ PDF Chat	Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems	1974	C. Antoniak	3