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Found 6 papers, 3 papers with code
Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs
On complex downstream tasks with limited data, such as fluid flow simulations and fluid-structure interactions, we found CoDA-NO to outperform existing methods on the few-shot learning task by over $36\%$.
Neural Operators with Localized Integral and Differential Kernels
In this work, we present a principled approach to operator learning that can capture local features under two frameworks by learning differential operators and integral operators with locally supported kernels.
A Practical Probabilistic Benchmark for AI Weather Models
We also reveal how multiple time-step loss functions, which many data-driven weather models have employed, are counter-productive: they improve deterministic metrics at the cost of increased dissipation, deteriorating probabilistic skill.
ACE: A fast, skillful learned global atmospheric model for climate prediction
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency.
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning.
Generating Liquid Simulations with Deformation-aware Neural Networks
Our algorithm captures these complex phenomena in two stages: a first neural network computes a weighting function for a set of pre-computed deformations, while a second network directly generates a deformation field for refining the surface.
