Multifidelity deep operator networks for data-driven and physics-informed problems

2023/09/01 by Amanda A. Howard, Mauro Perego, George Em Karniadakis +1 · 67 citations
Engineering · Physics and Astronomy · #Artificial intelligence #Computer science #Deep learning #Fidelity #Fluid Dynamics and Vibration Analysis #High fidelity #Machine learning #Model Reduction and Neural Networks #Nuclear Engineering Thermal-Hydraulics #Operator (biology) #Physics

paper · open access · doi:10.1016/j.jcp.2023.112462

published in Journal of Computational Physics 493, 112462 (Elsevier BV)

openalex publication_date 2023/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Citations

• Exponential Time Differencing for Stiff Systems
• A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
• Transfer learning based multi-fidelity physics informed deep neural network

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