Targeted use of deep learning for physics-informed model discovery by Nathan Kutz скачать в хорошем качестве

Targeted use of deep learning for physics-informed model discovery by Nathan Kutz

Nathan Kutz (University of Washington), "Targeted use of deep learning for physics-informed model discovery" https://faculty.washington.edu/kutz/ Abstract: Machine learning and artificial intelligence algorithms are now being used to automate the discovery of governing physical equations and coordinate systems from measurement data alone. However, positing a universal physical law from data is challenging: (i) An appropriate coordinate system must also be advocated and (ii) simultaneously proposing an accompanying discrepancy model to account for the inevitable mismatch between theory and measurements must be considered. Using a combination of deep learning and sparse regression, specifically the sparse identification of nonlinear dynamics (SINDy) algorithm, we show how a robust mathematical infrastructure can be formulated for simultaneously learning physics models and their coordinate systems. This can be done with limited data and sensors. We demonstrate the methods on a diverse number of examples, showing how data can maximally be exploited for scientific and engineering applications. AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences, March 22-24, 2021 (https://sites.google.com/view/aaai-mlps​) Papers: https://sites.google.com/view/aaai-ml... Slides: https://sites.google.com/view/aaai-ml...