New open source release from my team at Google: Dinosaur, a differentiable dynamical core for global atmospheric modeling, written in JAX: https://t.co/5WASoZ1GLM
Dinosaur is a core component of NeuralGCM and we hope it is useful for the weather/climate research community.
We have a paper in ICLR! The title is “Learned Coarse Models for Efficient Turbulence Simulation.” We wanted to see if we could train general-purpose ML models to predict turbulent dynamics accurately at low spatial and temporal resolutions (1/n).
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Key learnings:
1) Graph-nets learn amplitudes and phases that generalize to larger lattice sizes
2) Learning phases and amplitudes with separate networks gives better results
3) GNNs have easier time restoring symmetries than learning in a fully symmetric subspace
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Our work "Learning ground states of quantum Hamiltonians with graph networks" is out!
https://t.co/XSIj574c8Z
TLDR: we do Variational Monte Carlo on Heisenberg Hamiltonians using graph-nets as variational ansatz.
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@spectralhippo
Alvaro Sanchez
@PeterWBattaglia@bkclark4
Can machine learning improve physics constrained optimization tasks, like those at the heart of numerical weather forecasting?
Happy to share some our work on "Variational Data Assimilation with a Learned Inverse Observation Operator", tomorrow/today at ICML.
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Come here our selected contributed talk on simulating turbulence with deep learning at #SimDL (https://t.co/0PnK0SbAat) today
Led by @neuro_kim, Alvaro Sanchez, @DrumBushField, Dmitrii Kochkov, @MilesCranmer, @spectralhippo, Jonathan Goodwin, Elaine Cui and @PeterWBattaglia
@MilesCranmer I think once the particle symmetry is introduced we are forced to make "sets" out of elements of the corresponding Fock space. With that axiom of choice still feel natural.
The paper “Learning Mesh-Based Simulation with Graph Networks” shows how to effectively learn physics simulation on irregular meshes. The model can simulate vastly different systems with significant speedups over the ground truth solver: https://t.co/MxW36V4TtY #ICLR2021 (3/3)
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Excited to share "Machine learning accelerated computational fluid dynamics"
https://t.co/8rXhLGTVZC
We use ML inside a CFD simulator to advance the accuracy/speed Pareto frontier
with/
Jamie A. Smith
Ayya Alieva
Qing Wang
Michael P. Brenner
@shoyer
@ngutten Great question! We don't have any results on this yet, but it's an important area to advance. One challenge with boundaries is that the flow is often very different near the walls and might require additional augmentations.
@ColinJMcAuliffe That's a good perspective! In fact the goal of such effective "regularization" is to minimize errors caused by the loss of details on coarse grids.
One thing to note is that our approach is unlikely to model fully missing detail since we achieve high pointwise accuracy.
@whiskeyandwry Great question! My take on this is that it depends heavily on the application. One could use such accelerated methods to perform design by optimization and then evaluate the performance using methods with stronger convergence guarantees.