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Data-based Discovery of Governing Equations
[article]
2020
arXiv
pre-print
Most common mechanistic models are traditionally presented in mathematical forms to explain a given physical phenomenon. Machine learning algorithms, on the other hand, provide a mechanism to map the input data to output without explicitly describing the underlying physical process that generated the data. We propose a Data-based Physics Discovery (DPD) framework for automatic discovery of governing equations from observed data. Without a prior definition of the model structure, first a
arXiv:2012.06036v2
fatcat:2jtqz2srofeinoaxj7wz3in2qe