Research highlights

In addition, you will find a number of research highlights around the topic of "Deep Learning Approaches for Numerical PDEs"

Ch. Beck and S. Becker and Ph. Grohs and A. Jentzen
Solving stochastic differential equations and Kolmogorov equations by means of deep learning, in review, SAM Report 2018-21

Ch. Beck and W. E and A. Jentzen
Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations, in review, J. Nonlinear Sci., SAM Report 2017-49

J. Han and A. Jentzen and W. E.
Solving high-dimensional partial differential equations using deep learning, accepted (2017), Proc. Natl. Acad. Sci., SAM Report 2017-44

W. E and J. Han and A. Jentzen
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations, Communications in Mathematics and Statistics, 5/4 (2017), pp. 349-380, SAM Report 2017-29

S. Mishra
A machine learning framework for data driven acceleration of computations of differential equations, in review, SAM Report 2018-28

Ch. Schwab and J. Zech
Deep Learning in High Dimension, accepted (2018), Analysis and Applications, Singapore, SAM Report 2017-57