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Discovery of Partial Differential Equations from Highly Noisy and Sparse Data with Physics-Informed Information Criterion

, , and . Research, (2023)
DOI: 10.34133/research.0147

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Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data., , and . CoRR, (2020)Discovery of Partial Differential Equations from Highly Noisy and Sparse Data with Physics-Informed Information Criterion, , and . Research, (2023)Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion., , and . CoRR, (2022)Constructing Sub-scale Surrogate Model for Proppant Settling in Inclined Fractures from Simulation Data with Multi-fidelity Neural Network., , , and . CoRR, (2021)Numerical method for compressible gas-particle flow coupling using adaptive parcel refinement (APR) method on non-uniform mesh., , , , and . J. Comput. Phys., (2022)Compressible multiphase particle-in-cell method (CMP-PIC) for full pattern flows of gas-particle system., , , , , and . J. Comput. Phys., (2020)Theory-guided hard constraint projection (HCP): A knowledge-based data-driven scientific machine learning method., , , , , , and . J. Comput. Phys., (2021)Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data., , , and . CoRR, (2021)Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method., , , , , , and . CoRR, (2020)