Physics-Informed Neural Networks: Theory and Applications

Author:

Anitescu Cosmin,İsmail Ateş Burak,Rabczuk Timon

Publisher

Springer International Publishing

Reference71 articles.

1. Abadi M, Agarwal A, Barham P, Brevdo E et al (2015) TensorFlow: large scale machine learning on heterogeneous systems. Software available from tensorflow.org. https://www.tensorflow.org/

2. Agostinelli F, Hoffman M, Sadowski P, Baldi P (2014) Learning acti vation functions to improve deep neural networks. arXiv:1412.6830

3. Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T (2019) Artificial neural network methods for the solution of second order boundary value problems. Comput Mater Continua 59(1):345–359

4. Apicella A, Donnarumma F, Isgr‘o F, Prevete R (2021) A survey on modern trainable activation functions. Neural Netw 138:14–32

5. Bin Waheed U, Haghighat E, Alkhalifah T, Song C et al (2021) PINNeik: Eikonal solution using physics-informed neural networks. Comput Geosci 155:104833

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