activity
20132026
most citedData-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks

355 citations · 1.5k across the 64 of their papers we have counts for

collaborators

68 papers

cs.LG2026

Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events

Isabella S. Thiel, Juan Bello-Rivas, Yannis G. Kevrekidis +1

Extreme events in chaotic systems are difficult to learn from short trajectories because they are controlled by transient finite-time instability rather than by frequently observed…

eess.SY2026

A Time-to-Collision Barrier Function Approach to Collision Avoidance for Stochastic Systems

Benedikt Barthel Sorensen, Mitchell Black, Erfaun Noorani +1

Collision avoidance constraints for autonomous systems are typically formulated in position or velocity space, implicitly reacting to geometric proximity. We propose an alternative…

eess.SY2026

A Temporal Barrier Framework for Collision Avoidance in Multi-Agent Autonomous Aerial Vehicles

Benedikt Barthel Sorensen, Mitchell Black, Erfaun Noorani +1

Operating teams of autonomous aircraft in dynamic, uncertain, and potentially adversarial environments requires safety protocols that are reliable yet selective, and allow agents t…

nlin.CD2026

Risk-Sensitive Learning in Population Games under Extreme Events: Bifurcations and Chaotic Dynamics

Konstantinos Metaxas, Themistoklis P. Sapsis

Inspired by nonequilibrium phenomena in game dynamics and behavioral evidence on the impact of extreme events on decision making, we investigate the nonlinear dynamics of a discret…

cs.LG2026

Dynamics-Informed Deep Learning for Predicting Extreme Events

Eirini Katsidoniotaki, Themistoklis P. Sapsis

Predicting extreme events in high-dimensional chaotic dynamical systems remains a fundamental challenge, as such events are rare, intermittent, and arise from transient dynamical m…

stat.ML2025

Extreme Event Aware (-) Learning

Kai Chang, Themistoklis P. Sapsis

Quantifying and predicting rare and extreme events is challenging because such events are infrequent, severe, and expensive to simulate. Existing data-driven methods often require…