Reference: | Cont, R., & Voltchkova, E. (2006). A finite difference scheme for option pricing in jump diffusion and exponential lévy models. SIAM Journal on Numerical Analysis, 43(4), 1596–1626. https://doi.org/10.1137/S0036142903436186 Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2, 303–314. https://doi.org/10.1007/BF02551274 Leshno, M., Lin, V. Y., Pinkus, A., & Schocken, S. (1993). Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Networks, 6(6), 861–867. https://doi.org/10.1016/S0893-6080(05)80131-5 Lu, L., Meng, X., Mao, Z., & Karniadakis, G. E. (2021). Deepxde: A deep learning library for solving differential equations. SIAM Review, 63(1), 208–228. https://doi.org/10.1137/ 19M1274067 Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378(1), 686–707. https: //doi.org/10.1016/j.jcp.2018.10.045 Schwartz, E. (1977). The valuation of warrants: Implementing a new approach. Journal of Financial Economics, 4, 79–93. https://doi.org/10.1016/0304-405X(77)90037-X 30 |