activity
20242026
most citedA non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics

1 citations · 2 across the 3 of their papers we have counts for

collaborators

4 papers

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…

physics.comp-ph2025★ 1 cited

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…

physics.ao-ph2024★ 1 cited

A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics

Benedikt Barthel Sorensen, Alexis Charalampopoulos, Shixuan Zhang +3

Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully-resolved climate simulations remain com…