A Taylor Based Sampling Scheme for Machine Learning in Computational Physics

• URL: http://arxiv.org/abs/2101.11105v2
• Date: Thu, 28 Jan 2021 12:48:18 GMT
• Title: A Taylor Based Sampling Scheme for Machine Learning in Computational Physics
• Authors: Paul Novello (CEA, Inria, X), Ga\"el Po\"ette (CEA), David Lugato (CEA), Pietro Congedo (Inria, X)
• Abstract summary: We take advantage of the ability to generate data using numerical simulations programs to train Machine Learning models better. We elaborate a new data sampling scheme based on Taylor approximation to reduce the error of a Deep Neural Network (DNN) when learning the solution of an ordinary differential equations (ODE) system.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine Learning (ML) is increasingly used to construct surrogate models for physical simulations. We take advantage of the ability to generate data using numerical simulations programs to train ML models better and achieve accuracy gain with no performance cost. We elaborate a new data sampling scheme based on Taylor approximation to reduce the error of a Deep Neural Network (DNN) when learning the solution of an ordinary differential equations (ODE) system.

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