A DeepParticle method for learning and generating aggregation patterns in multi-dimensional Keller-Segel chemotaxis systems. (arXiv:2209.00109v1 [physics.comp-ph])

We study a regularized interacting particle method for computing aggregation
patterns and near singular solutions of a Keller-Segal (KS) chemotaxis system
in two and three space dimensions, then further develop DeepParticle (DP)
method to learn and generate solutions under variations of physical parameters.
The KS solutions are approximated as empirical measures of particles which
self-adapt to the high gradient part of solutions. We utilize the
expressiveness of deep neural networks (DNNs) to represent the transform of
samples from a given …

aggregation arxiv learning patterns physics systems