Themistoklis P. Sapsis

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 36

Top Topics

Recurrent Neural Networks

Dynamical Systems

Importance Sampling

Top Venues

J. Comput. Phys.

SIAM/ASA J. Uncertain. Quantification

Publications

Yorgos M. Psarellis, Themistoklis P. Sapsis, Ioannis G. Kevrekidis
Active search for Bifurcations. CoRR (2024)
Benedikt Barthel Sorensen, Alexis-Tzianni Charalampopoulos, Shixuan Zhang, Bryce Harrop, Ruby Leung, Themistoklis P. Sapsis
A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics. CoRR (2024)
Eirini Katsidoniotaki, Biao Su, Eleni Kelasidi, Themistoklis P. Sapsis
Multifidelity digital twin for real-time monitoring of structural dynamics in aquaculture net cages. CoRR (2024)
Stephen Guth, Alireza Mojahed, Themistoklis P. Sapsis
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems. CoRR (2023)
Jiaqi Zhang, Louis Cammarata, Chandler Squires, Themistoklis P. Sapsis, Caroline Uhler
Active learning for optimal intervention design in causal models. Nat. Mac. Intell. 5 (10) (2023)
Ethan Pickering, Themistoklis P. Sapsis
Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models. CoRR (2022)
Ethan Pickering, Themistoklis P. Sapsis
Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression. CoRR (2022)
Antoine Blanchard, Nishant Parashar, Boyko A. Dodov, Christian Lessig, Themistoklis P. Sapsis
A Multi-Scale Deep Learning Framework for Projecting Weather Extremes. CoRR (2022)
Ethan Pickering, Stephen Guth, George Em Karniadakis, Themistoklis P. Sapsis
Discovering and forecasting extreme events via active learning in neural operators. Nat. Comput. Sci. 2 (12) (2022)
Ethan Pickering, George Em Karniadakis, Themistoklis P. Sapsis
Discovering and forecasting extreme events via active learning in neural operators. CoRR (2022)
Hassan Arbabi, Themistoklis P. Sapsis
Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics. SIAM/ASA J. Uncertain. Quantification 10 (2) (2022)
Jiaqi Zhang, Louis Cammarata, Chandler Squires, Themistoklis P. Sapsis, Caroline Uhler
Active Learning for Optimal Intervention Design in Causal Models. CoRR (2022)
Antoine Blanchard, Themistoklis P. Sapsis
Bayesian optimization with output-weighted optimal sampling. J. Comput. Phys. 425 (2021)
Zhong Y. Wan, Boyko A. Dodov, Christian Lessig, Henk Dijkstra, Themistoklis P. Sapsis
A data-driven framework for the stochastic reconstruction of small-scale features with application to climate data sets. J. Comput. Phys. 442 (2021)
Yibo Yang, Antoine Blanchard, Themistoklis P. Sapsis, Paris Perdikaris
Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs. CoRR (2021)
Antoine Blanchard, Themistoklis P. Sapsis
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification. SIAM/ASA J. Uncertain. Quantification 9 (2) (2021)
Samuel H. Rudy, Themistoklis P. Sapsis
Output-weighted and relative entropy loss functions for deep learning precursors of extreme events. CoRR (2021)
Alexis-Tzianni Charalampopoulos, Spencer H. Bryngelson, Tim Colonius, Themistoklis P. Sapsis
Hybrid quadrature moment method for accurate and stable representation of non-Gaussian processes and their dynamics. CoRR (2021)
Antoine Blanchard, Themistoklis P. Sapsis
Output-Weighted Importance Sampling for Bayesian Experimental Design and Uncertainty Quantification. CoRR (2020)
Antoine Blanchard, Themistoklis P. Sapsis
Informative Path Planning for Anomaly Detection in Environment Exploration and Monitoring. CoRR (2020)
Antoine Blanchard, Themistoklis P. Sapsis
Bayesian Optimization with Output-Weighted Importance Sampling. CoRR (2020)
Samuel H. Rudy, Themistoklis P. Sapsis
Sparse Methods for Automatic Relevance Determination. CoRR (2020)
Pantelis R. Vlachas, Jaideep Pathak, Brian R. Hunt, Themistoklis P. Sapsis, Michelle Girvan, Edward Ott, Petros Koumoutsakos
Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics. Neural Networks 126 (2020)
Antoine Blanchard, Themistoklis P. Sapsis
Machine Learning the Tangent Space of Dynamical Instabilities from Data. CoRR (2019)
Antoine Blanchard, Themistoklis P. Sapsis
Analytical Description of Optimally Time-Dependent Modes for Reduced-Order Modeling of Transient Instabilities. SIAM J. Appl. Dyn. Syst. 18 (2) (2019)
Stephen Guth, Themistoklis P. Sapsis
Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems. Entropy 21 (10) (2019)
Pantelis R. Vlachas, Jaideep Pathak, Brian R. Hunt, Themistoklis P. Sapsis, Michelle Girvan, Edward Ott, Petros Koumoutsakos
Forecasting of Spatio-temporal Chaotic Dynamics with Recurrent Neural Networks: a comparative study of Reservoir Computing and Backpropagation Algorithms. CoRR (2019)
Themistoklis P. Sapsis
Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples. CoRR (2019)
Saviz Mowlavi, Themistoklis P. Sapsis
Model Order Reduction for Stochastic Dynamical Systems with Continuous Symmetries. SIAM J. Sci. Comput. 40 (3) (2018)
Mustafa A. Mohamad, Themistoklis P. Sapsis
A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems. CoRR (2018)
Pantelis R. Vlachas, Wonmin Byeon, Zhong Y. Wan, Themistoklis P. Sapsis, Petros Koumoutsakos
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long-Short Term Memory Networks. CoRR (2018)
Han Kyul Joo, Mustafa A. Mohamad, Themistoklis P. Sapsis
Extreme events and their optimal mitigation in nonlinear structural systems excited by stochastic loads: Application to ocean engineering systems. CoRR (2017)
Hessam Babaee, Minseok Choi, Themistoklis P. Sapsis, George E. Karniadakis
A robust bi-orthogonal/dynamically-orthogonal method using the covariance pseudo-inverse with application to stochastic flow problems. J. Comput. Phys. 344 (2017)
Mohammad Farazmand, Themistoklis P. Sapsis
Reduced-order prediction of rogue waves in two-dimensional deep-water waves. J. Comput. Phys. 340 (2017)
Mustafa A. Mohamad, Will Cousins, Themistoklis P. Sapsis
A probabilistic decomposition-synthesis method for the quantification of rare events due to internal instabilities. J. Comput. Phys. 322 (2016)
Mustafa A. Mohamad, Themistoklis P. Sapsis
Probabilistic Description of Extreme Events in Intermittently Unstable Dynamical Systems Excited by Correlated Stochastic Processes. SIAM/ASA J. Uncertain. Quantification 3 (1) (2015)
Minseok Choi, Themistoklis P. Sapsis, George E. Karniadakis
On the equivalence of dynamically orthogonal and bi-orthogonal methods: Theory and numerical simulations. J. Comput. Phys. 270 (2014)
Mustafa A. Mohamad, Themistoklis P. Sapsis
Analytical Approximation of the Heavy-Tail Structure for Intermittently Unstable Complex Modes. DyDESS (2014)
Will Cousins, Themistoklis P. Sapsis
Reduced Order Probabilistic Prediction of Rogue Waves in One-Dimensional Envelope Equations. DyDESS (2014)
Mattheus P. Ueckermann, Pierre F. J. Lermusiaux, Themistoklis P. Sapsis
Numerical schemes for dynamically orthogonal equations of stochastic fluid and ocean flows. J. Comput. Phys. 233 (2013)
Minseok Choi, Themistoklis P. Sapsis, George E. Karniadakis
A convergence study for SPDEs using combined Polynomial Chaos and Dynamically-Orthogonal schemes. J. Comput. Phys. 245 (2013)
Themistoklis P. Sapsis, Andrew J. Majda
Blending Modified Gaussian Closure and Non-Gaussian Reduced Subspace Methods for Turbulent Dynamical Systems. J. Nonlinear Sci. 23 (6) (2013)
George Haller, Themistoklis P. Sapsis
Localized Instability and Attraction along Invariant Manifolds. SIAM J. Appl. Dyn. Syst. 9 (2) (2010)