Fractional physics-informed neural networks for time-fractional phase field models

2022/08/20 by Shupeng Wang, Hui Zhang, Xiaoyun Jiang · 1 citation
Engineering · Materials Science · Mathematics · Physics and Astronomy · #Applied mathematics #Collocation (remote sensing) #Collocation method #Computer science #Differential equation #Discretization #Field (mathematics) #Fractional calculus #Geometry #Inverse #Inverse problem #Magnetic Properties and Applications #Mathematical analysis #Mathematics #Model Reduction and Neural Networks #Nanofluid Flow and Heat Transfer #Spectral method

paper · doi:10.1007/s11071-022-07746-3

openalex publication_date 2022/08/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Citations

• Application of a fractional advection‐dispersion equation
• DIFFUSE-INTERFACE METHODS IN FLUID MECHANICS
• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Machine Learning for Fluid Mechanics
• PPINN: Parareal physics-informed neural network for time-dependent PDEs
• Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

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