Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems

• URL: http://arxiv.org/abs/2204.09157v2
• Date: Tue, 21 Nov 2023 05:06:33 GMT
• Title: Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems
• Authors: Amanda A. Howard, Mauro Perego, George E. Karniadakis, Panos Stinis
• Abstract summary: We present a composite Deep Operator Network (DeepONet) for learning using two datasets with different levels of fidelity. We demonstrate the new multi-fidelity training in diverse examples, including modeling of the ice-sheet dynamics of the Humboldt glacier, Greenland.
• Score: 0.9999629695552196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Operator learning for complex nonlinear systems is increasingly common in modeling multi-physics and multi-scale systems. However, training such high-dimensional operators requires a large amount of expensive, high-fidelity data, either from experiments or simulations. In this work, we present a composite Deep Operator Network (DeepONet) for learning using two datasets with different levels of fidelity to accurately learn complex operators when sufficient high-fidelity data is not available. Additionally, we demonstrate that the presence of low-fidelity data can improve the predictions of physics-informed learning with DeepONets. We demonstrate the new multi-fidelity training in diverse examples, including modeling of the ice-sheet dynamics of the Humboldt glacier, Greenland, using two different fidelity models and also using the same physical model at two different resolutions.

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