- Published
- 2021-06-02
- Venue
- Computer Methods in Applied Mechanics and Engineering, vol. 384, pp. 113938
- Cited by
- 704
- Open access
- Yes, bronze
- DOI
- 10.1016/j.cma.2021.113938
Cite this paper
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- APA 7
Wang, S., Wang, H., & Perdikaris, P. G. (2021). On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks. Computer Methods in Applied Mechanics and Engineering, 384, 113938. https://doi.org/10.1016/j.cma.2021.113938
- MLA 9
Wang, Sifan, et al. "On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks." Computer Methods in Applied Mechanics and Engineering, vol. 384, 2021, pp. 113938, https://doi.org/10.1016/j.cma.2021.113938.
- Chicago
Wang, Sifan, Hanwen Wang, and Paris G. Perdikaris. "On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks." Computer Methods in Applied Mechanics and Engineering 384 (2021): 113938. https://doi.org/10.1016/j.cma.2021.113938.
- GOST
Wang S., Wang H., Perdikaris P. G. On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks // Computer Methods in Applied Mechanics and Engineering. — 2021. — Vol. 384. — P. 113938. — URL: https://doi.org/10.1016/j.cma.2021.113938
- BibTeX
@article{wang2021eigenvector, title = {{On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks}}, author = {Sifan Wang and Hanwen Wang and Paris G. Perdikaris}, journal = {Computer Methods in Applied Mechanics and Engineering}, year = {2021}, volume = {384}, pages = {113938}, doi = {10.1016/j.cma.2021.113938}, url = {https://doi.org/10.1016/j.cma.2021.113938} }- RIS
TY - JOUR AU - Wang, Sifan AU - Wang, Hanwen AU - Perdikaris, Paris G. TI - On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks JO - Computer Methods in Applied Mechanics and Engineering VL - 384 SP - 113938 PY - 2021 DA - 2021/06/02 DO - 10.1016/j.cma.2021.113938 UR - https://doi.org/10.1016/j.cma.2021.113938 ER -