Neural algorithm for solving differential equations
By Hyuk Lee, I. Kang. 1990
Finite difference equations are considered to solve differential equations numerically by utilizing minimization algorithms. Neural minimization algorithms for solving the finite difference equations are presented. Results of numerical simulation are described to demonstrate the method. Methods of implementing the algorithms are discussed. General features of the neural algorithms are discussed.
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Citations in the corpus
- Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations (2017, unknown): “…Chiaramonte and Kiener 2013; Dehghan et al. 2009; E et al. 2017c; Han and E 2016; Han et al. 2017; Khoo et al. 2017; Krizhevsky et al. 2012; Lagaris et al. 1998; LeCun et al. 1998, 2015; Lee and Kang 1990; Meade and Fernández 1994; Mehrkanoon and Suykens 2015; Ramuhalli et al. 2005; Ruder 2016).”
- “Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology (2019, background): “For example, deep learning canoutperformdensity functional theory (DFT) in quantum chemistry [28], and in solving ordinary and partial differential equations [12, 14].”