Funder
U.S. Department of Energy
United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research
United States Department of Defense | United States Navy | Office of Naval Research
Publisher
Springer Science and Business Media LLC
Reference39 articles.
1. Lu, L., Jin, P., Pang, G., Zhang, Z. & Karniadakis, G. E. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nature Mach. Intell. 3, 218–229 (2021).
2. Li, Z. et al. Fourier neural operator for parametric partial differential equations. In Proc. 2021 International Conference on Learning Representation (ICLR, 2021).
3. Lu, L. et al. A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data. Comput. Methods Appl. Mech. Eng. 393, 114778 (2022).
4. Kontolati, K., Goswami, S., Shields, M. D. & Karniadakis, G. E. On the influence of over-parameterization in manifold based surrogates and deep neural operators. J. Comput. Phys. 479, 112008 (2023).
5. Cao, Q., Goswami, S., Tripura, T., Chakraborty, S. & Karniadakis, G. E. Deep neural operators can predict the real-time response of floating offshore structures under irregular waves. Comput. Struct. 291, 107228 (2024).
Cited by
3 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