vardhah/Batch-mode-DeepAL-for-regression
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression
This tool helps engineers quickly develop accurate prediction models for complex designs in fields like aerospace and marine engineering. It takes limited, costly simulation data (e.g., from FEA or CFD) and produces an efficient deep learning model that can predict performance outcomes with significantly less data than traditional methods, saving days of simulation time. It's designed for engineering professionals who need to create 'surrogate models' to approximate the behavior of computationally expensive simulations.
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Use this if you need to build accurate predictive models for engineering designs, where generating simulation data (like from FEA, CFD, or propeller design tools) is extremely time-consuming and expensive.
Not ideal if your primary goal is not engineering design optimization or if you have abundant, inexpensive data that doesn't require strategic sampling.
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Jupyter Notebook
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GPL-3.0
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Last pushed
Dec 30, 2022
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