A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes

DOI10.4171/RLM/943zbMath1498.65150OpenAlexW4200500918WikidataQ115211930 ScholiaQ115211930MaRDI QIDQ2075654

Stefano Pagani, Alessandro Cosenza, Francesco Regazzoni, Alessandro Lombardi, Alfio M. Quarteroni

Publication date: 15 February 2022

Published in: Atti della Accademia Nazionale dei Lincei. Classe di Scienze Fisiche, Matematiche e Naturali. Serie IX. Rendiconti Lincei. Matematica e Applicazioni (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.4171/rlm/943

zbMATH Keywords

optimizationpartial differential equationsmachine learningmultifidelityphysics-informed neural network

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Neural biology (92C20) Physiology (general) (92C30) Cell biology (92C37) Numerical methods for inverse problems for initial value and initial-boundary value problems involving PDEs (65M32) Numerical methods for ill-posed problems for initial value and initial-boundary value problems involving PDEs (65M30) PDE constrained optimization (numerical aspects) (49M41)

Related Items

Multifidelity deep operator networks for data-driven and physics-informed problems ⋮ Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations

Uses Software

• SciPy
• TensorFlow
• Keras
• Adam
• GitHub
• PINNsNTK

Cites Work

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