Publisher
Springer Science and Business Media LLC
Reference35 articles.
1. Beck, C., Hutzenthaler, M., Jentzen, A., & Kuckuck, B. (2020). An overview on deep learning-based approximation methods for partial differential equations. arXiv:2012.12348.
2. Beck, C., Becker, S., Cheridito, P., Jentzen, A., & Neufeld, A. (2021). Deep splitting method for parabolic PDEs. SIAM Journal on Scientific Computing, 43(5), A3135–A3154.
3. Beck, C., E, W., & Jentzen, A. (2019). 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.
4. Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(2).
5. Chen, T., & Chen, H. (1995). Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems. IEEE Transactions on Neural Networks, 6(4), 911–917.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献