Abstract
With the success of deep learning technology in many application areas, there have been pioneering approaches to approximate solutions of partial differential equations by neural network functions [2, 10, 12, 13]. Such approaches have advantages over the classical approximation methods in that they can be used without generating meshes adaptive to problem domains or developing equation dependent numerical schemes.
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The first author is supported by NRF-2022R1A2C100388511
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Kim, H.H., Yang, H.J. (2024). Domain Decomposition Algorithms for Neural Network Approximation of Partial Differential Equations. In: Dostál, Z., et al. Domain Decomposition Methods in Science and Engineering XXVII. DD 2022. Lecture Notes in Computational Science and Engineering, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-50769-4_3
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