https://github.com/leehyeonbeen/experiments

Computational experiments for my research
https://github.com/leehyeonbeen/experiments

Last synced: about 2 months ago
JSON representation

Computational experiments for my research

• Host: GitHub
• URL: https://github.com/leehyeonbeen/experiments
• Owner: leehyeonbeen
• License: mit
• Created: 2023-02-03T12:17:09.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-02-06T07:12:51.000Z (over 2 years ago)
• Last Synced: 2025-02-08T02:11:11.711Z (3 months ago)
• Language: Python
• Homepage:
• Size: 27.4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# **Transformers**

- *Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.*

# **Fourier Neural Operator**

- *Kovachki, N., Li, Z., Liu, B., Azizzadenesheli, K., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2021). Neural operator: Learning maps between function spaces. arXiv preprint arXiv:2108.08481.*

- *Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2020). Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895.*

# **SINDy**

- *de Silva, B. M., Champion, K., Quade, M., Loiseau, J. C., Kutz, J. N., & Brunton, S. L. (2020). Pysindy: a python package for the sparse identification of nonlinear dynamics from data. arXiv preprint arXiv:2004.08424.*

- *Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016). Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the national academy of sciences, 113(15), 3932-3937.*  

# **MFcNN**

- *Meng, X., & Karniadakis, G. E. (2020). A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems. Journal of Computational Physics, 401, 109020.*