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Found 3 papers, 1 papers with code
Enabling Automatic Differentiation with Mollified Graph Neural Operators
It can also solve PDEs on unstructured point clouds seamlessly, using physics losses only, at resolutions vastly lower than those needed for finite differences to be accurate enough.
Tensor-GaLore: Memory-Efficient Training via Gradient Tensor Decomposition
We present Tensor-GaLore, a novel method for efficient training of neural networks with higher-order tensor weights.
A Library for Learning Neural Operators
We present NeuralOperator, an open-source Python library for operator learning.
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