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Deep transfer operator learning for partial differential equations under conditional shift - Nature Machine Intelligence

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Deep transfer operator learning for partial differential equations under conditional shift - Nature Machine Intelligence

Transfer learning enables the transfer of knowledge gained while learning to perform one task (source) to a related but different task (target), hence addressing the expense of data acquisition and labelling, potential computational power limitations and dataset distribution mismatches. We propose a new transfer learning framework for task-specific learning (functional regression in partial differential equations) under conditional shift based on the deep operator network (DeepONet).

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Deep transfer operator learning for partial differential equations under conditional shift - Nature Machine Intelligence

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Deep transfer operator learning for partial differential equations under conditional shift - Nature Machine Intelligence

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