Uncertainty Quantification in Neural Differential Equations

• URL: http://arxiv.org/abs/2111.04207v1
• Date: Mon, 8 Nov 2021 00:09:39 GMT
• Title: Uncertainty Quantification in Neural Differential Equations
• Authors: Olga Graf, Pablo Flores, Pavlos Protopapas, Karim Pichara
• Abstract summary: Uncertainty quantification (UQ) helps to make trustworthy predictions based on collected observations and uncertain domain knowledge. We adapt several state-of-the-art UQ methods to get the predictive uncertainty for DE solutions.
• Score: 3.291910356217187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uncertainty quantification (UQ) helps to make trustworthy predictions based on collected observations and uncertain domain knowledge. With increased usage of deep learning in various applications, the need for efficient UQ methods that can make deep models more reliable has increased as well. Among applications that can benefit from effective handling of uncertainty are the deep learning based differential equation (DE) solvers. We adapt several state-of-the-art UQ methods to get the predictive uncertainty for DE solutions and show the results on four different DE types.

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