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Found 8 papers, 5 papers with code
Neural General Circulation Models for Weather and Climate
Here we present the first GCM that combines a differentiable solver for atmospheric dynamics with ML components, and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best ML and physics-based methods.
Learning to correct spectral methods for simulating turbulent flows
We build upon Fourier-based spectral methods, which are known to be more efficient than other numerical schemes for simulating PDEs with smooth and periodic solutions.
Learned Coarse Models for Efficient Turbulence Simulation
We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the comparably low resolutions across various scientifically relevant metrics.
Learning ground states of quantum Hamiltonians with graph networks
In this work we use graph neural networks to define a structured variational manifold and optimize its parameters to find high quality approximations of the lowest energy solutions on a diverse set of Heisenberg Hamiltonians.
Learned Simulators for Turbulence
We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the same low resolutions across various scientifically relevant metrics.
Variational Data Assimilation with a Learned Inverse Observation Operator
Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data.
Machine learning accelerated computational fluid dynamics
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics.
Learned discretizations for passive scalar advection in a 2-D turbulent flow
The computational cost of fluid simulations increases rapidly with grid resolution.
Computational Physics Disordered Systems and Neural Networks Fluid Dynamics
