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Stiff Neural Ordinary Differential Equations
[article]
2021
arXiv
pre-print
Neural Ordinary Differential Equations (ODE) are a promising approach to learn dynamic models from time-series data in science and engineering applications. This work aims at learning Neural ODE for stiff systems, which are usually raised from chemical kinetic modeling in chemical and biological systems. We first show the challenges of learning neural ODE in the classical stiff ODE systems of Robertson's problem and propose techniques to mitigate the challenges associated with scale separations
arXiv:2103.15341v3
fatcat:ocaccsxhrre6bgbzi6mdksl76e