1.1k citations · 1.7k across the 13 of their papers we have counts for
13 papers
AIMIP Phase 1: systematic evaluations of AI weather and climate models
Brian Henn, Christopher S. Bretherton, Nikolay Koldunov +18
We present the AI weather and climate model intercomparison project (AIMIP), phase 1. Drawing from the rich tradition of intercomparisons in climate model development, we specify a…
Neural general circulation models optimized to predict satellite-based precipitation observations
Janni Yuval, Ian Langmore, Dmitrii Kochkov +1
Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. Here, we present a hybrid model that is trained directly on satellite-bas…
Neural General Circulation Models for Weather and Climate
Dmitrii Kochkov, Janni Yuval, Ian Langmore +13
General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics…
Learning to correct spectral methods for simulating turbulent flows
Gideon Dresdner, Dmitrii Kochkov, Peter Norgaard +4
Despite their ubiquity throughout science and engineering, only a handful of partial differential equations (PDEs) have analytical, or closed-form solutions. This motivates a vast…
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…
Learning ground states of quantum Hamiltonians with graph networks
Dmitrii Kochkov, Tobias Pfaff, Alvaro Sanchez-Gonzalez +2
Solving for the lowest energy eigenstate of the many-body Schrodinger equation is a cornerstone problem that hinders understanding of a variety of quantum phenomena. The difficulty…
Variational Data Assimilation with a Learned Inverse Observation Operator
Thomas Frerix, Dmitrii Kochkov, Jamie A. Smith +3
Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data. The physical model can subsequently be evolved i…
Machine learning accelerated computational fluid dynamics
Dmitrii Kochkov, Jamie A. Smith, Ayya Alieva +3
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics. Fluids are well described by…
Learned discretizations for passive scalar advection in a 2-D turbulent flow
Jiawei Zhuang, Dmitrii Kochkov, Yohai Bar-Sinai +2
The computational cost of fluid simulations increases rapidly with grid resolution. This has given a hard limit on the ability of simulations to accurately resolve small scale feat…
Variational optimization in the AI era: Computational Graph States and Supervised Wave-function Optimization
Dmitrii Kochkov, Bryan K. Clark
Representing a target quantum state by a compact, efficient variational wave-function is an important approach to the quantum many-body problem. In this approach, the main challeng…
Classical phase diagram of the stuffed honeycomb lattice
Jyotisman Sahoo, Dmitrii Kochkov, Bryan K. Clark +1
We investigate the classical phase diagram of the stuffed honeycomb Heisenberg lattice, which consists of a honeycomb lattice with a superimposed triangular lattice formed by sites…
The mother of all states of the kagome quantum antiferromagnet
Hitesh J. Changlani, Dmitrii Kochkov, Krishna Kumar +2
Frustrated quantum magnets are a central theme in condensed matter physics due to the richness of their phase diagrams. They support a panoply of phases including various ordered s…
Deviation from the dipole-ice model in the new spinel spin-ice candidate, MgErSe
D. Reig-i-Plessis, S. V. Geldern, A. A. Aczel +3
In spin ice research, small variations in structure or interactions drive a multitude of different behaviors, yet the collection of known materials relies heavily on the `227' pyro…