Methods and papers related to physics-based maching learning for modeling stochastic reaction networks, as developed in the thesis of Oliver K. Ernst

Eric Mjolsness Departments of Computer Science and Mathematics, UC Irvine

Click here to browse all code through this organization's GitHub page.

Modeling reaction-diffusion systems with dynamic Boltzmann distributions

Physics-based machine learning for modeling stochastic IP3-dependent calcium dynamics

Deep learning moment closure approximations using dynamic Boltzmann distributions

Learning moment closure in reaction-diffusion systems with spatial dynamic Boltzmann distributions

Learning dynamic Boltzmann distributions as reduced models of spatial chemical kinetics

- Graph-constrained Correlation Dynamics (GCCD)

Various codes attached to papers (see above).

TensorFlow Python package for physics-based dynamic PCA

C++ library for Dynamic Boltzmann Machines for Lattice Chemical Kinetics

C++ library for Gillespie simulations on a lattice

C++ library for Gillespie simulations

Python package to sample pairs of particles from discrete Gaussians