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Found 5 papers, 2 papers with code
Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion
The PIC is also employed to discover unrevealed macroscale governing equations from microscopic simulation data in an actual physical scene.
Constructing Sub-scale Surrogate Model for Proppant Settling in Inclined Fractures from Simulation Data with Multi-fidelity Neural Network
The results demonstrate that constructing the settling surrogate with the MFNN can reduce the need for high-fidelity data and thus computational cost by 80%, while the accuracy lost is less than 5% compared to a high-fidelity surrogate.
Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data
Although deep-learning has been successfully applied in a variety of science and engineering problems owing to its strong high-dimensional nonlinear mapping capability, it is of limited use in scientific knowledge discovery.
Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
Machine learning models have been successfully used in many scientific and engineering fields.
Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data
Next, genetic algorithm is utilized to discover the form of PDEs and corresponding coefficients with an incomplete candidate library.
