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Found 5 papers, 1 papers with code
On-Manifold Projected Gradient Descent
We use these tools to obtain adversarial examples that reside on a class manifold, yet fool a classifier.
Constructing coarse-scale bifurcation diagrams from spatio-temporal observations of microscopic simulations: A parsimonious machine learning approach
For our illustrations, we implemented the proposed method to construct the one-parameter bifurcation diagram of the 1D FitzHugh-Nagumo PDEs from data generated by $D1Q3$ Lattice Boltzmann simulations.
Coarse-grained and emergent distributed parameter systems from data
We explore the derivation of distributed parameter system evolution laws (and in particular, partial differential operators and associated partial differential equations, PDEs) from spatiotemporal data.
Particles to Partial Differential Equations Parsimoniously
Equations governing physico-chemical processes are usually known at microscopic spatial scales, yet one suspects that there exist equations, e. g. in the form of Partial Differential Equations (PDEs), that can explain the system evolution at much coarser, meso- or macroscopic length scales.
