stefaniafresca/POD-DL-ROM
Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition. Available on doi.org/10.1016/j.cma.2021.114181.
This project helps engineers and scientists more efficiently model complex physical systems described by time-dependent partial differential equations (PDEs), especially those involving nonlinear behavior. It takes high-fidelity simulation data of these systems and produces a faster, more computationally efficient model that can predict system behavior for new parameters without running full simulations. This is useful for researchers and engineers working on problems in fields like fluid dynamics, heat transfer, or structural mechanics.
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Use this if you need to rapidly predict the behavior of complex, nonlinear physical systems for various input parameters, without the high computational cost of running full-scale simulations every time.
Not ideal if your systems are simple, linear, or if you don't have existing high-fidelity simulation data to train the models.
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Sep 07, 2023
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