PDEBench and PDE-Net
About PDEBench
pdebench/PDEBench
PDEBench: An Extensive Benchmark for Scientific Machine Learning
This project provides a comprehensive benchmark for evaluating machine learning models designed to solve Partial Differential Equations (PDEs). It offers a wide range of realistic physical problems, along with ready-to-use datasets containing various initial/boundary conditions and PDE parameters. Scientists, engineers, and researchers working with scientific machine learning can use this to compare and develop methods for simulating complex physical phenomena.
About PDE-Net
ZichaoLong/PDE-Net
PDE-Net: Learning PDEs from Data
This helps scientists and engineers discover the underlying partial differential equations (PDEs) that govern observed phenomena. You provide time-series or spatial data from an experiment or simulation, and it outputs the mathematical PDE model that describes the data's behavior. This is ideal for researchers in physics, engineering, and other quantitative fields who need to derive governing equations from observations.
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