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Found 5 papers, 1 papers with code
Data-based Discovery of Governing Equations
Without a prior definition of the model structure, first a free-form of the equation is discovered, and then calibrated and validated against the available data.
Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets
The presented PLoM constrained by PDE allows for generating a large number of learned realizations of the stochastic process and its corresponding random control parameter.
Normal-bundle Bootstrap
Probabilistic models of data sets often exhibit salient geometric structure.
Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset
This paper tackles the challenge presented by small-data to the task of Bayesian inference.
Data-driven discovery of free-form governing differential equations
We present a method of discovering governing differential equations from data without the need to specify a priori the terms to appear in the equation.
