Universal differential equations for scientific machine learning
Christopher Rackauckas, Yingbo Ma, Julius Martensen, Collin Warner, Kirill Zubov, Rohit Supekar, Dominic Skinner, Ali Ramadhan, Alan Edelman
Paper
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arXiv preprint arXiv:2001.04385, 2020
Notes
Once a trajectory has been recovered, riva2026random proposes fitting Universal Differential Equations from Rackauckas et al. so that the polynomial RDPG architecture supplies known mechanistic structure while a neural component absorbs residual dynamics, with adjoint sensitivities providing gradients through the ODE solver.
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