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Real Time Stress Prediction Using Machine Learning

Statistical modeling techniques are gaining popularity as surrogate models to mechanistic modeling such as finite element analyses (FEA). The surrogate aims to map inputs to outputs while the FEA estimates the solution using the governing equations. Previous work has shown that machine learning algorithms have modeled highly non-linear data. Additionally, transfer learning has been a method applied in computer vision and natural language processing to reuse common knowledge learnt from a particular task to a different, but related task. However, the application of such implementations to engineering simulations is only starting to emerge.

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Presented at the NAFEMS World Congress 2021, www.nafems.org/congress