7 papers
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…
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…
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…
Learning Dissipative Chaotic Dynamics with Boundedness Guarantees
Sunbochen Tang, Themistoklis Sapsis, Navid Azizan
Chaotic dynamics, commonly seen in weather systems and fluid turbulence, are characterized by their sensitivity to initial conditions, which makes accurate prediction challenging.…
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…
GEN2: A Generative Prediction-Correction Framework for Long-time Emulations of Spatially-Resolved Climate Extremes
Mengze Wang, Benedikt Barthel Sorensen, Themistoklis Sapsis
Accurately quantifying the increased risks of climate extremes requires generating large ensembles of climate realization across a wide range of emissions scenarios, which is compu…