基于物理的机器学习建模随机IP3-dependent钙动力学
Physics-based machine learning for modeling stochastic IP3-dependent calcium dynamics
摘要：
我们提出了一个机器学习方法模型简化，通过候选函数结合特定领域的物理。我们的方法估计一个有效的概率分布和微分方程模型的反应网络的随机模拟。缩小和精细尺度描述之间的密切联系允许从主方程导出的近似被引入到学习问题中。这种表示被证明是提高泛化，并允许一个经典的模型，三磷酸肌醇（IP3）依赖钙振荡在非兴奋细胞的网络大小的大幅度减少。
英文摘要：
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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