A deep learning approach to the probabilistic numerical solution of path-dependent partial differential equations
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computational Mathematics,Numerical Analysis,Analysis
Link
https://link.springer.com/content/pdf/10.1007/s42985-023-00255-x.pdf
Reference34 articles.
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3. Becker, S., Braunwarth, R., Hutzenthaler, M., Jentzen, A., von Wurstemberger, Ph.: Numerical simulations for full history recursive multilevel Picard approximations for systems of high-dimensional partial differential equations. Commun. Comput. Phys. 28(5), 2109–2138 (2020)
4. Beck, C., E, W., Jentzen, A: Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations. J. Nonlinear Sci. 29(4), 1563–1619 (2019)
5. Dupire, B.: Functional Itô calculus (2009). Available at SSRN: https://ssrn.com/abstract=1435551 or https://doi.org/10.2139/ssrn.1435551
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