Physics-informed Mesh-independent Deep Compositional Operator Network

arXiv:2404.13646v1 Announce Type: cross
Abstract: Solving parametric Partial Differential Equations (PDEs) for a broad range of parameters is a critical challenge in scientific computing. To this end, neural operators, which learn mappings from parameters to solutions, have been successfully used. However, the training of neural operators typically demands large training datasets, the acquisition of which can be prohibitively expensive. To address this challenge, physics-informed training can offer a cost-effective strategy. However, current physics-informed neural operators face limitations, either in handling …

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