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Universal Differential Equations for Scientific Machine Learning

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A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions., , , , and . HPEC, page 1-9. IEEE, (2021)Parallelizing Explicit and Implicit Extrapolation Methods for Ordinary Differential Equations., , , , and . HPEC, page 1-9. IEEE, (2022)DifferentialEquations.jl–A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia, and . Journal of Open Research Software, 5 (1): 15 (May 2017)Universal Differential Equations for Scientific Machine Learning, , , , , , , , and . arXiv:2001.04385 cs.LG, (2021)Sum-of-Squares Bounds for Quantum Optimal Control., , , , and . QCE, page 365-366. IEEE, (2023)Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks., , , , , , and . AAAI Spring Symposium: MLPS, volume 2964 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)Universal Differential Equations for Scientific Machine Learning, , , , , , , , and . (2020)cite arxiv:2001.04385Comment: 3 figures, 2 tables, 3 supplemental figures, 27 pages, 18 supplemental pages.Parallelizing Explicit and Implicit Extrapolation Methods for Ordinary Differential Equations., , , and . CoRR, (2022)Bayesian Neural Ordinary Differential Equations., , , , and . CoRR, (2020)Composing Modeling and Simulation with Machine Learning in Julia., , , , , , , , , and 3 other author(s). CoRR, (2021)