3 papers
cs.LG2026
Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignment
Kai Nelson, Tobias Kreiman, Sergey Levine +1
A fundamental challenge in science and engineering is the simulation-to-experiment gap. While we often possess prior knowledge of physical laws, these physical laws can be too diff…
cs.LG2024
EventFlow: Forecasting Temporal Point Processes with Flow Matching
Gavin Kerrigan, Kai Nelson, Padhraic Smyth
Continuous-time event sequences, in which events occur at irregular intervals, are ubiquitous across a wide range of industrial and scientific domains. The contemporary modeling pa…
physics.ao-ph2024
A Generative Diffusion Model for Probabilistic Ensembles of Precipitation Maps Conditioned on Multisensor Satellite Observations
Clement Guilloteau, Gavin Kerrigan, Kai Nelson +4
A generative diffusion model is used to produce probabilistic ensembles of precipitation intensity maps at the 1-hour 5-km resolution. The generation is conditioned on infrared and…