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Found 3 papers, 3 papers with code
Sparse Methods for Automatic Relevance Determination
This work considers methods for imposing sparsity in Bayesian regression with applications in nonlinear system identification.
Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
A critical challenge in the data-driven modeling of dynamical systems is producing methods robust to measurement error, particularly when data is limited.
Numerical Analysis
Data-driven discovery of partial differential equations
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain.
Pattern Formation and Solitons
