Universal Differential Equations for Scientific Machine Learning

https://arxiv.org/abs/2001.04385

by formalsystem • 6 years ago

3 1 6 years ago

ipnon• 6 years ago

Significance statement:

"Incorporation of prior scientific knowledge into learnable structures could allow for a new form of machine learning that is data-efficient: thereby side stepping the issue of acquiring large expensive datasets by utilizing the condensed knowledge of the scientific literature. This manuscript develops a novel learning framework, the universal differential equation, which allows for machine-learning-augmented scientific models. We showcase the ability to incorporate prior scientific knowledge and train accurate neural architectures with small data. We demonstrate how the method is interpretable [sic] back to mechanistic equations and how it can accelerate climate simulations by 15,000x. The broad applications coupled with a software implementation demonstrates a viable path for small-data scientific machine learning."

Universal Differential Equations for Scientific Machine Learning

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