Search Results for author: Dmitrii Kochkov

Found 8 papers, 5 papers with code

Neural General Circulation Models for Weather and Climate

1 code implementation13 Nov 2023 Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith, Griffin Mooers, Milan Klöwer, James Lottes, Stephan Rasp, Peter Düben, Sam Hatfield, Peter Battaglia, Alvaro Sanchez-Gonzalez, Matthew Willson, Michael P. Brenner, Stephan Hoyer

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.

Physical Simulations Weather Forecasting

Learning to correct spectral methods for simulating turbulent flows

2 code implementations1 Jul 2022 Gideon Dresdner, Dmitrii Kochkov, Peter Norgaard, Leonardo Zepeda-Núñez, Jamie A. Smith, Michael P. Brenner, Stephan Hoyer

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.

BIG-bench Machine Learning

Learned Coarse Models for Efficient Turbulence Simulation

1 code implementation31 Dec 2021 Kimberly Stachenfeld, Drummond B. Fielding, Dmitrii Kochkov, Miles Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter Battaglia, Alvaro Sanchez-Gonzalez

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

no code implementations12 Oct 2021 Dmitrii Kochkov, Tobias Pfaff, Alvaro Sanchez-Gonzalez, Peter Battaglia, Bryan K. Clark

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

no code implementations ICLR 2022 Kim Stachenfeld, Drummond Buschman Fielding, Dmitrii Kochkov, Miles Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter Battaglia, Alvaro Sanchez-Gonzalez

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

1 code implementation22 Feb 2021 Thomas Frerix, Dmitrii Kochkov, Jamie A. Smith, Daniel Cremers, Michael P. Brenner, Stephan Hoyer

Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data.

Weather Forecasting

Machine learning accelerated computational fluid dynamics

no code implementations28 Jan 2021 Dmitrii Kochkov, Jamie A. Smith, Ayya Alieva, Qing Wang, Michael P. Brenner, Stephan Hoyer

Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics.

BIG-bench Machine Learning

Learned discretizations for passive scalar advection in a 2-D turbulent flow

2 code implementations11 Apr 2020 Jiawei Zhuang, Dmitrii Kochkov, Yohai Bar-Sinai, Michael P. Brenner, Stephan Hoyer

The computational cost of fluid simulations increases rapidly with grid resolution.

Computational Physics Disordered Systems and Neural Networks Fluid Dynamics

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