Hybrid acceleration techniques for the physics-informed neural networks: a comparative analysis
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s10994-023-06442-6.pdf
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3. Berg, J., & Nyström, K. (2017). A unified deep artificial neural network approach to partial differential equations in complex geometries. https://doi.org/10.1016/j.neucom.2018.06.056. arXiv:1711.06464.
4. Cai, S., Wang, Z., Wang, S., Perdikaris, P., & Karniadakis, G. E. (2021). Physics-informed neural networks for heat transfer problems. Journal of Heat Transfer, 143(6), 060801. https://doi.org/10.1115/1.4050542
5. Chen, Y., Lu, L., Karniadakis, G. E., & Negro, L. D. (2019). Physics-informed neural networks for inverse problems in nano-optics and metamaterials. arXiv:1912.01085 [physics.comp-ph].
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