SciANN: a keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks. In this paper, we introduce SciANN, a Python package for scientific computing and physics-informed deep learning using artificial neural networks. SciANN uses the widely used deep-learning packages TensorFlow and Keras to build deep neural networks and optimization models, thus inheriting many of Keras’s functionalities, such as batch optimization and model reuse for transfer learning. SciANN is designed to abstract neural network construction for scientific computations and solution and discovery of partial differential equations (PDE) using the physics-informed neural networks (PINN) architecture, therefore providing the flexibility to set up complex functional forms. We illustrate, in a series of examples, how the framework can be used for curve fitting on discrete data, and for solution and discovery of PDEs in strong and weak forms. We summarize the features currently available in SciANN, and also outline ongoing and future developments.

References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Kovacs, Alexander; Exl, Lukas; Kornell, Alexander; Fischbacher, Johann; Hovorka, Markus; Gusenbauer, Markus; Breth, Leoni; Oezelt, Harald; Yano, Masao; Sakuma, Noritsugu; Kinoshita, Akihito; Shoji, Tetsuya; Kato, Akira; Schrefl, Thomas: Conditional physics informed neural networks (2022)
  2. Mo, Yifan; Ling, Liming; Zeng, Delu: Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm (2022)
  3. Amini Niaki, Sina; Haghighat, Ehsan; Campbell, Trevor; Poursartip, Anoush; Vaziri, Reza: Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture (2021)
  4. Haghighat, Ehsan; Bekar, Ali Can; Madenci, Erdogan; Juanes, Ruben: A nonlocal physics-informed deep learning framework using the peridynamic differential operator (2021)
  5. Haghighat, Ehsan; Juanes, Ruben: SciANN: a keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (2021)
  6. Haghighat, Ehsan; Raissi, Maziar; Moure, Adrian; Gomez, Hector; Juanes, Ruben: A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (2021)
  7. Kharazmi, Ehsan; Zhang, Zhongqiang; Karniadakis, George E. M.: \textithp-VPINNs: variational physics-informed neural networks with domain decomposition (2021)
  8. Olivier, Audrey; Shields, Michael D.; Graham-Brady, Lori: Bayesian neural networks for uncertainty quantification in data-driven materials modeling (2021)
  9. Oune, Nicholas; Bostanabad, Ramin: Latent map Gaussian processes for mixed variable metamodeling (2021)
  10. Wei Peng, Jun Zhang, Weien Zhou, Xiaoyu Zhao, Wen Yao, Xiaoqian Chen: IDRLnet: A Physics-Informed Neural Network Library (2021) arXiv