Assessing the Performance of PINN and CNN Approaches in Solving the 1D Burgers' Equation with Deep Learning Architectures
Author:
Affiliation:
1. School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3650215.3650370
Reference10 articles.
1. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
2. Raissi, M., Perdikaris, P., Karniadakis, G.E. 2017. Physics informed deep learning (part I): data-driven solutions of nonlinear partial differential equations, arXiv preprint, arXiv:1711.10561, 2017.
3. Cai S. Mao Z. Wang Z. Yin M. Karniadakis G.E. 2022. Physics-informed neural networks (PINNs) for fluid mechanics: a review Acta Mech. Sin. https://doi.org/10.1007/s10409-021-01148-1.
4. ImageNet classifcation with deep convolutional neural networks;Krizhevsky A.;Adv Neural Inf Process Syst,2012
5. Raissi, M., Karniadakis, G.E. 2017. Hidden physics models: machine learning of nonlinear partial differential equations, 2017, arXiv:1708.00588.
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