Deep Neural Network Learning for PDE Solutions

Wei Cai ()
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Wei Cai: Southern Methodist University, Department of Mathematics

Chapter Chapter 6 in Deterministic, Stochastic, and Deep Learning Methods for Computational Electromagnetics, 2025, pp 161-186 from Springer

Abstract: Abstract Deep neural networks (DNNs) employ successive function compositions of affine mappings and nonlinear activation function operations to create approximations for general functions. The coefficients in the affine mappings comprise the neural networks’ trainable parameters in machine learning methods.

Date: 2025
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DOI: 10.1007/978-981-96-0100-4_6

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