# Non-linear Independent Dual System (NIDS) for Discretization-independent Surrogate Modeling over Complex Geometries

@article{Duvall2021NonlinearID, title={Non-linear Independent Dual System (NIDS) for Discretization-independent Surrogate Modeling over Complex Geometries}, author={J. Michael Duvall and Karthik Duraisamy and Shaowu Pan}, journal={ArXiv}, year={2021}, volume={abs/2109.07018} }

Numerical solution of partial differential equations (PDEs) require expensive simulations, limiting their application in design optimization routines, model-based control, or solution of large-scale inverse problems. Existing Convolutional Neural Network-based frameworks for surrogate modeling require lossy pixelization and data-preprocessing, which is not suitable for realistic engineering applications. Therefore, we propose non-linear independent dual system (NIDS), which is a deep learning… Expand

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