Physics-based machine learning for modeling stochastic IP3-dependent calcium dynamics
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Abstract (arXiv)
We present a machine learning method for model reduction which incorporates domain-specific physics through candidate functions. Our method estimates an effective probability distribution and differential equation model from stochastic simulations of a reaction network. The close connection between reduced and fine scale descriptions allows approximations derived from the master equation to be introduced into the learning problem. This representation is shown to improve generalization and allows a large reduction in network size for a classic model of inositol trisphosphate (IP3) dependent calcium oscillations in non-excitable cells.
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References · 6
- 2017PDE-Net: Learning PDEs from Datacited by 304
- 2018Discovering physical concepts with neural networkscited by 117
- 2017Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledgecited by 95
- 2018Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systemscited by 88
- 2014Bird Species Categorization Using Pose Normalized Deep Convolutional Netscited by 50
- 2019Deep Learning Moment Closure Approximations using Dynamic Boltzmann Distributionscited by 1
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