research
∙
06/01/2023
Training neural operators to preserve invariant measures of chaotic attractors
Chaotic systems make long-horizon forecasts difficult because small pert...
share
research
∙
05/31/2023
Deep Stochastic Mechanics
This paper introduces a novel deep-learning-based approach for numerical...
research
∙
03/20/2023
Model Stitching: Looking For Functional Similarity Between Representations
Model stitching (Lenc Vedaldi 2015) is a compelling methodology to c...
research
∙
02/23/2023
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Studying the dynamics of open quantum systems holds the potential to ena...
research
∙
08/31/2022
Discovering Conservation Laws using Optimal Transport and Manifold Learning
Conservation laws are key theoretical and practical tools for understand...
research
∙
07/01/2022
Deep Learning and Symbolic Regression for Discovering Parametric Equations
Symbolic regression is a machine learning technique that can learn the g...
research
∙
07/22/2021
Discovering Sparse Interpretable Dynamics from Partial Observations
Identifying the governing equations of a nonlinear dynamical system is k...
research
∙
04/23/2021
Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems
Bayesian optimization (BO) is a popular paradigm for global optimization...
research
∙
07/13/2019
