26 citations · 43 across the 4 of their papers we have counts for
4 papers
Learning Integrable Dynamics with Action-Angle Networks
Ameya Daigavane, Arthur Kosmala, Miles Cranmer +2
Machine learning has become increasingly popular for efficiently modelling the dynamics of complex physical systems, demonstrating a capability to learn effective models for dynami…
TNT: Vision Transformer for Turbulence Simulations
Yuchen Dang, Zheyuan Hu, Miles Cranmer +2
Turbulence is notoriously difficult to model due to its multi-scale nature and sensitivity to small perturbations. Classical solvers of turbulence simulation generally operate on f…
Rediscovering orbital mechanics with machine learning
Pablo Lemos, Niall Jeffrey, Miles Cranmer +2
We present an approach for using machine learning to automatically discover the governing equations and hidden properties of real physical systems from observations. We train a "gr…
Learned Coarse Models for Efficient Turbulence Simulation
Kimberly Stachenfeld, Drummond B. Fielding, Dmitrii Kochkov +7
Turbulence simulation with classical numerical solvers requires high-resolution grids to accurately resolve dynamics. Here we train learned simulators at low spatial and temporal r…