Localized Instability and Attraction along Invariant Manifolds. - George Haller; Themistoklis P. Sapsis (SIAM J. Appl. Dyn. Syst., 2010)

A convergence study for SPDEs using combined Polynomial Chaos and Dynamically-Orthogonal schemes. - Minseok Choi; Themistoklis P. Sapsis; George E. Karniadakis (J. Comput. Phys., 2013)

Numerical schemes for dynamically orthogonal equations of stochastic fluid and ocean flows. - Mattheus P. Ueckermann; Pierre F. J. Lermusiaux; Themistoklis P. Sapsis (J. Comput. Phys., 2013)

Blending Modified Gaussian Closure and Non-Gaussian Reduced Subspace Methods for Turbulent Dynamical Systems. - Themistoklis P. Sapsis; Andrew J. Majda (J. Nonlinear Sci., 2013)

Reduced Order Probabilistic Prediction of Rogue Waves in One-Dimensional Envelope Equations. - Will Cousins; Themistoklis P. Sapsis (DyDESS, 2014)

Analytical Approximation of the Heavy-Tail Structure for Intermittently Unstable Complex Modes. - Mustafa A. Mohamad; Themistoklis P. Sapsis (DyDESS, 2014)

On the equivalence of dynamically orthogonal and bi-orthogonal methods: Theory and numerical simulations. - Minseok Choi; Themistoklis P. Sapsis; George E. Karniadakis (J. Comput. Phys., 2014)

Probabilistic Description of Extreme Events in Intermittently Unstable Dynamical Systems Excited by Correlated Stochastic Processes. - Mustafa A. Mohamad; Themistoklis P. Sapsis (SIAM/ASA J. Uncertain. Quantification, 2015)

A probabilistic decomposition-synthesis method for the quantification of rare events due to internal instabilities. - Mustafa A. Mohamad; Will Cousins; Themistoklis P. Sapsis (J. Comput. Phys., 2016)

Extreme events and their optimal mitigation in nonlinear structural systems excited by stochastic loads: Application to ocean engineering systems. - Han Kyul Joo; Mustafa A. Mohamad; Themistoklis P. Sapsis (CoRR, 2017)

A robust bi-orthogonal/dynamically-orthogonal method using the covariance pseudo-inverse with application to stochastic flow problems. - Hessam Babaee; Minseok Choi; Themistoklis P. Sapsis; George E. Karniadakis (J. Comput. Phys., 2017)

Reduced-order prediction of rogue waves in two-dimensional deep-water waves. - Mohammad Farazmand; Themistoklis P. Sapsis (J. Comput. Phys., 2017)

Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long-Short Term Memory Networks. - Pantelis-Rafail Vlachas; Wonmin Byeon; Zhong Yi Wan; Themistoklis P. Sapsis; Petros Koumoutsakos (CoRR, 2018)

A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems. - Mustafa A. Mohamad; Themistoklis P. Sapsis (CoRR, 2018)

Model Order Reduction for Stochastic Dynamical Systems with Continuous Symmetries. - Saviz Mowlavi; Themistoklis P. Sapsis (SIAM J. Sci. Comput., 2018)

Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples. - Themistoklis P. Sapsis (CoRR, 2019)

Machine Learning the Tangent Space of Dynamical Instabilities from Data. - Antoine Blanchard; Themistoklis P. Sapsis (CoRR, 2019)

Forecasting of Spatio-temporal Chaotic Dynamics with Recurrent Neural Networks: a comparative study of Reservoir Computing and Backpropagation Algorithms. - Pantelis-Rafail Vlachas; Jaideep Pathak; Brian R. Hunt; Themistoklis P. Sapsis; Michelle Girvan; Edward Ott; Petros Koumoutsakos (CoRR, 2019)

Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems. - Stephen Guth; Themistoklis P. Sapsis (Entropy, 2019)

Analytical Description of Optimally Time-Dependent Modes for Reduced-Order Modeling of Transient Instabilities. - Antoine Blanchard; Themistoklis P. Sapsis (SIAM J. Appl. Dyn. Syst., 2019)

Bayesian Optimization with Output-Weighted Importance Sampling. - Antoine Blanchard; Themistoklis P. Sapsis (CoRR, 2020)

Sparse Methods for Automatic Relevance Determination. - Samuel H. Rudy; Themistoklis P. Sapsis (CoRR, 2020)

Informative Path Planning for Anomaly Detection in Environment Exploration and Monitoring. - Antoine Blanchard; Themistoklis P. Sapsis (CoRR, 2020)

Output-Weighted Importance Sampling for Bayesian Experimental Design and Uncertainty Quantification. - Antoine Blanchard; Themistoklis P. Sapsis (CoRR, 2020)

Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics. - Pantelis-Rafail Vlachas; Jaideep Pathak; Brian R. Hunt; Themistoklis P. Sapsis; Michelle Girvan; Edward Ott; Petros Koumoutsakos (Neural Networks, 2020)

Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs. - Yibo Yang; Antoine Blanchard; Themistoklis P. Sapsis; Paris Perdikaris (CoRR, 2021)

Hybrid quadrature moment method for accurate and stable representation of non-Gaussian processes and their dynamics. - Alexis-Tzianni Charalampopoulos; Spencer H. Bryngelson; Tim Colonius; Themistoklis P. Sapsis (CoRR, 2021)

Output-weighted and relative entropy loss functions for deep learning precursors of extreme events. - Samuel H. Rudy; Themistoklis P. Sapsis (CoRR, 2021)

Bayesian optimization with output-weighted optimal sampling. - Antoine Blanchard; Themistoklis P. Sapsis (J. Comput. Phys., 2021)

A data-driven framework for the stochastic reconstruction of small-scale features with application to climate data sets. - Zhong Yi Wan; Boyko A. Dodov; Christian Lessig; Henk Dijkstra; Themistoklis P. Sapsis (J. Comput. Phys., 2021)

Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification. - Antoine Blanchard; Themistoklis P. Sapsis (SIAM/ASA J. Uncertain. Quantification, 2021)

Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression. - Ethan Pickering; Themistoklis P. Sapsis (CoRR, 2022)

Discovering and forecasting extreme events via active learning in neural operators. - Ethan Pickering; George Em Karniadakis; Themistoklis P. Sapsis (CoRR, 2022)

Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models. - Ethan Pickering; Themistoklis P. Sapsis (CoRR, 2022)

A Multi-Scale Deep Learning Framework for Projecting Weather Extremes. - Antoine Blanchard; Nishant Parashar; Boyko A. Dodov; Christian Lessig; Themistoklis P. Sapsis (CoRR, 2022)

Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics. - Hassan Arbabi; Themistoklis P. Sapsis (SIAM/ASA J. Uncertain. Quantification, 2022)

Discovering and forecasting extreme events via active learning in neural operators. - Ethan Pickering; Stephen Guth; George Em Karniadakis; Themistoklis P. Sapsis (Nat. Comput. Sci., 2022)

Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems. - Stephen Guth; Alireza Mojahed; Themistoklis P. Sapsis (CoRR, 2023)

Active learning for optimal intervention design in causal models. - Jiaqi Zhang; Louis Cammarata; Chandler Squires; Themistoklis P. Sapsis; Caroline Uhler (Nat. Mac. Intell., 2023)

A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics. - Benedikt Barthel Sorensen; Alexis-Tzianni Charalampopoulos; Shixuan Zhang; Bryce Harrop; Ruby Leung; Themistoklis P. Sapsis (CoRR, 2024)

Multifidelity digital twin for real-time monitoring of structural dynamics in aquaculture net cages. - Eirini Katsidoniotaki; Biao Su; Eleni Kelasidi; Themistoklis P. Sapsis (CoRR, 2024)

Active search for Bifurcations. - Yorgos M. Psarellis; Themistoklis P. Sapsis; Ioannis G. Kevrekidis (CoRR, 2024)

A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data. - Benedikt Barthel Sorensen; Leonardo Zepeda-Núñez; Ignacio Lopez-Gomez; Zhong Yi Wan; Rob Carver; Fei Sha; Themistoklis P. Sapsis (CoRR, 2024)

Sampling Low-Fidelity Outputs for Estimation of High-Fidelity Density and Its Tails. - Minji Kim; Kevin O'Connor; Vladas Pipiras; Themistoklis P. Sapsis (SIAM/ASA J. Uncertain. Quantification, 2025)

On Some Tunable Multi-fidelity Bayesian Optimization Frameworks. - Arjun Manoj; Anastasia S. Georgiou; Dimitris G. Giovanis; Themistoklis P. Sapsis; Ioannis G. Kevrekidis (CoRR, 2025)