
This paper proposes an effective data generation-based operator learning method to solve partial differential equations (PDEs), including nonlinear cases, defined on unbounded domains.


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efficient-data-driven-operator-learning-for-solving-partial-differential-equations-on-unbounded-domains


Efficient Data-Driven Operator Learning for Solving Partial Differential Equations on Unbounded Domains


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A novel distributed training approach enables a single neural operator with significantly fewer parameters to effectively tackle multi-operator learning challenges, without incurring additional average costs.


efficient-multi-operator-learning-with-distributed-neural-operators


Efficient Multi-Operator Learning with Distributed Neural Operators



The author introduces DPOT, a novel auto-regressive denoising pre-training strategy based on Fourier attention for large-scale PDE pre-training. This approach aims to enhance performance on downstream tasks by leveraging diverse PDE datasets.


dpot-auto-regressive-denoising-operator-transformer-for-large-scale-pde-pre-training


DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
