
Mathematics in Engineering, 2019, 1(1): 118146. doi: 10.3934/Mine.2018.1.118.
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A machine learning framework for data driven acceleration of computations of differential equations
Seminar for Applied Mathematics (SAM), DMath, ETH Zürich, Rämistrasse 101, Zürich8092,Switzerland
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Keywords: machine learning; deep learning; differential equations; nonconvex optimization; timedependent problems
Citation: Siddhartha Mishra. A machine learning framework for data driven acceleration of computations of differential equations. Mathematics in Engineering, 2019, 1(1): 118146. doi: 10.3934/Mine.2018.1.118
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This article has been cited by:
 1. Kjetil O. Lye, Siddhartha Mishra, Deep Ray, Deep learning observables in computational fluid dynamics, Journal of Computational Physics, 2020, 109339, 10.1016/j.jcp.2020.109339
 2. Martin Hutzenthaler, Arnulf Jentzen, Thomas Kruse, Tuan Anh Nguyen, A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations, SN Partial Differential Equations and Applications, 2020, 1, 2, 10.1007/s4298501900069
 3. KJETIL O. LYE, SIDDHARTHA MISHRA, ROBERTO MOLINARO, A multilevel procedure for enhancing accuracy of machine learning algorithms, European Journal of Applied Mathematics, 2020, 1, 10.1017/S0956792520000224
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