Ultimately, a hybrid approach might win out in the end. Use equations for what we know, use machine learning to fit parameters in the equations, as well as ter

Ultimately, a hybrid approach might win out in the end. Use equations for what we know, use machine learning to fit parameters in the equations, as well as terms we don't know. Prople nowadays can solve differential equations where some terms are "neural networks" and train those networks on data while numerically solving the equation. (some people call it "neural differential equations", if you want a search query to start)