Meshless methods for American option pricing through Physics-Informed Neural Networks

The application of improved physics-informed neural network (IPINN) method in finance

(2022) Yuexing Bai et al.   NONLINEAR DYNAMICS

Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

(2022) Salvatore Cuomo et al.   JOURNAL OF SCIENTIFIC COMPUTING

Physics-Informed Neural Networks for Heat Transfer Problems

(2021) Shengze Cai et al.   JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME

Deep learning of free boundary and Stefan problems

(2020) Sifan Wang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Machine Learning for Semi Linear PDEs

(2019) Quentin Chan-Wai-Nam et al.   JOURNAL OF SCIENTIFIC COMPUTING

DGM: A deep learning algorithm for solving partial differential equations

(2018) Justin Sirignano et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Solving high-dimensional partial differential equations using deep learning

(2018) Jiequn Han et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Error bounds for approximations with deep ReLU networks

(2017) Dmitry Yarotsky   NEURAL NETWORKS

A comparative analysis of local meshless formulation for multi-asset option models

(2016) Siraj-ul-Islam et al.   ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS

Adaptive lattice methods for multi-asset models

(2008) Kyoung-Sook Moon et al.   COMPUTERS & MATHEMATICS WITH APPLICATIONS