Physics-based machine learning for stochastic reaction networks

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

Research groups:

Eric Mjolsness Departments of Computer Science and Mathematics, UC Irvine

Terrence Sejnowski & Tom Bartol Computational Neurobiology Lab, Salk Institute for Biological Studies

GitHub:

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

Thesis


Modeling reaction-diffusion systems with dynamic Boltzmann distributions

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Papers & Code


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

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Deep learning moment closure approximations using dynamic Boltzmann distributions

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

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Learning dynamic Boltzmann distributions as reduced models of spatial chemical kinetics

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Related work



Software


Various codes attached to papers (see above).


TensorFlow Python package for physics-based dynamic PCA

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

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C++ library for Gillespie simulations on a lattice

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C++ library for Gillespie simulations

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Python package to sample pairs of particles from discrete Gaussians

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