50 citations · 60 across the 8 of their papers we have counts for
4 papers · 1 filter
Training neural operators to preserve invariant measures of chaotic attractors
Ruoxi Jiang, Peter Y. Lu, Elena Orlova +1
Chaotic systems make long-horizon forecasts difficult because small perturbations in initial conditions cause trajectories to diverge at an exponential rate. In this setting, neura…
Deep Stochastic Mechanics
Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang +2
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schrödinger equation inspired by stochastic mechanics and generative diffusio…
Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting
Elena Orlova, Haokun Liu, Raphael Rossellini +2
Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting. This st…
Comparing the costs of abstraction for DL frameworks
Maksim Levental, Elena Orlova
High level abstractions for implementing, training, and testing Deep Learning (DL) models abound. Such frameworks function primarily by abstracting away the implementation details…