Today we’re talking to Matt Levine. Matt is a PhD student in computing and mathematical sciences at Caltech, and he focuses on improving the prediction and inference of physical systems by blending together both mechanistic modeling and machine learning.
This episode is one of my favorites: we go pretty deep into dynamical systems, and into Matt's new framework for solving them by blending traditional, mechanistic, approaches with machine learning. This is a fascinating use of machine learning, and hopefully gets us one step closer to the automation of science, in general.
A Framework for Machine Learning of Model Error in Dynamical Systems - https://arxiv.org/abs/2107.06658
Related works
Autodifferentiable Ensemble Kalman Filters - https://epubs.siam.org/doi/abs/10.1137/21M1434477
Universal Differential Equations for Scientific Machine Learning - https://arxiv.org/abs/2001.04385
Continuous-time nonlinear signal processing: a neural network based approach for gray box identification - https://ieeexplore.ieee.org/document/366006
A generalised approach to process state estimation using hybrid artificial neural network/mechanistic models - https://www.sciencedirect.com/science/article/abs/pii/S0098135496003365
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