Our work is based on https://github.com/hojunkim13/PINNs & https://github.com/PredictiveIntelligenceLab/MultiscalePINNs
This code has been tested with Pytorch 1.13.1, CUDA 11.7.1, Ubuntu 20.04. However, it would be fine with lower versions as well. The library versions used are:
numpy 1.24.3
scipy 1.10.1
matplotlib 3.7.1
pyDOE 0.3.8
pytorch 1.13.1
This project is licensed under the MIT License - see the LICENSE
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Raissi, Maziar, Paris Perdikaris, and George Em Karniadakis. "Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations." arXiv preprint arXiv:1711.10561 (2017).
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Raissi, Maziar, Paris Perdikaris, and George Em Karniadakis. "Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations." arXiv preprint arXiv:1711.10566 (2017).
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Raissi, Maziar, Paris Perdikaris, and George E. Karniadakis. "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations." Journal of Computational Physics 378 (2019): 686-707.
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S. Wang, H. Wang, P. Perdikaris."On the eigenvector bias of fourier feature networks: From re-gression to solving multi-scale pdes with physics-informed neural networks.", Computer Methods in Applied Mechanics and Engineering 384 (2021) 113938.