Scientific Machine Learning: Interpretable Neural Networks That Accurately Extrapolate From Small Data
January 14 2020 in Differential Equations, Julia, Mathematics, Science, Scientific ML | Tags: neural ode, physics-informed, sciml, small data, universal differential equations | Author: Christopher Rackauckas
The fundamental problems of classical machine learning are:
- Machine learning models require big data to train
- Machine learning models cannot extrapolate out of the their training data well
- Machine learning models are not interpretable
However, in our recent paper, we have shown that this does not have to be the case. In Universal Differential Equations for Scientific Machine Learning, we start by showing the following figure:
Indeed, it shows that by only seeing the tiny first part of the time series, we can automatically learn the equations in such a manner that it predicts the time series will be cyclic in the future, in a way … READ MORE