# SciANN: A Keras wrapper for scientific computations and physics-informed deep learning using artificial neural networks

@article{Haghighat2020SciANNAK, title={SciANN: A Keras wrapper for scientific computations and physics-informed deep learning using artificial neural networks}, author={E. Haghighat and R. Juanes}, journal={ArXiv}, year={2020}, volume={abs/2005.08803} }

In this paper, we introduce SciANN, a Python package for scientific computing and physicsinformed deep learning using artificial neural networks. SciANN uses the widely used deeplearning 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… Expand

#### 12 Citations

Physics-Informed Deep-Learning for Scientific Computing

- Computer Science
- ArXiv
- 2021

Evaluating the PINN potential to replace or accelerate traditional approaches for solving linear systems, and how to integrate PINN with traditional scientific computing approaches, such as multigrid and Gauss-Seidel methods. Expand

A deep learning framework for solution and discovery in solid mechanics: linear elasticity

- Mathematics, Computer Science
- ArXiv
- 2020

It is found that honoring the physics leads to improved robustness: when trained only on a few parameters, the PINN model can accurately predict the solution for a wide range of parameters new to the network—thus pointing to an important application of this framework to sensitivity analysis and surrogate modeling. Expand

A nonlocal physics-informed deep learning framework using the peridynamic differential operator

- Computer Science, Mathematics
- ArXiv
- 2020

Nonlocal PDDO-PINN is applied to the solution and identification of material parameters in solid mechanics and, specifically, to elastoplastic deformation in a domain subjected to indentation by a rigid punch, for which the mixed displacement--traction boundary condition leads to localized deformation and sharp gradients in the solution. Expand

Eikonal Solution Using Physics-Informed Neural Networks

- 2020

The eikonal equation is utilized across a wide spectrum of science and engineering disciplines. In seismology, it regulates seismic wave traveltimes needed for applications like source localization,… Expand

Robust Data-Driven Discovery of Partial Differential Equations under Uncertainties

- Mathematics, Computer Science
- ArXiv
- 2021

Results of numerical case studies indicate that the governing PDEs of many canonical dynamical systems can be correctly identified using the proposed ψ-PDE method with highly noisy data. Expand

NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework

- Computer Science, Physics
- ICCS
- 2021

The neural network solver methodology, the SimNet architecture, and the various features that are needed for effective solution of the PDEs are reviewed. Expand

SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets

- Computer Science, Physics
- ArXiv
- 2021

Deep Learning (DL) algorithms are emerging as a key alternative to computationally expensive CFD simulations. However, state-of-the-art DL approaches require large and highresolution training data to… Expand

An End-to-End AI-Driven Simulation Framework

- 2020

We present SimNet, an AI-driven multi-physics simulation framework aiming to accelerate simulations across a wide range of disciplines in science and engineering. Compared to traditional numerical… Expand

Deep learning for solution and inversion of structural mechanics and vibrations

- Computer Science, Mathematics
- ArXiv
- 2021

This chapter presents the application of deep learning and physics-informed neural networks concerning structural mechanics and vibration problems concerningStructural mechanics and vibrations problems. Expand

An energy-based error bound of physics-informed neural network solutions in elasticity

- Mathematics, Computer Science
- ArXiv
- 2020

An energy-based a posteriori error bound is proposed for the physics-informed neural network solutions of elasticity problems that provides an upper bound of the global error of neural network discretization. Expand

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