A two-phase sparse-regression method recovers interaction kernels of stochastic many-particle systems by fitting the residual of the mean-field PDE to kernel-density-estimated densities.
Multiscale modeling meets machine learning: What can we learn? Archives of Computational Methods in Engi- neering, 28:1017–1037, 2021
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Extracting Interaction Kernels for Many-Particle Systems by a Two-Phase Approach
A two-phase sparse-regression method recovers interaction kernels of stochastic many-particle systems by fitting the residual of the mean-field PDE to kernel-density-estimated densities.