Inspired by strategic discussions within the NAFEMS ASSESS Initiative, this curated finite element analysis dataset builds upon NAFEMS' 40-year legacy of establishing trusted industry benchmarks for physics-based simulation and looks to the emerging use of AI in engineering simulation.

The collection provides baseline data on principal stress distributions within plate geometries. A deliberately simple problem was selected to maximise the dataset's educational merit. Because the peak principal stress for this geometry can be validated against a well-established analytical solution (Heywood's equations), the dataset provides a transparent baseline. This simplicity allows engineers and researchers to study fundamental behaviour of the data driven models, such as demonstrating how the quantity and distribution of training data impacts the model predictions. As artificial intelligence models increasingly integrate into commercial simulation tools, this dataset is intended to serve as a useful resource for engineers looking for a vendor neutral dataset.

To maintain NAFEMS' commitment to vendor neutrality, the results are provided in the open standard VMAP format, alongside training and test data in HyperMesh (*.h3d) and Nastran (*.op2) formats. For those who wish to recreate the results on their own systems solver input decks have been provided in optistruct (*.fem) and Nastran (*.bdf) format.

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NAFEMS would like to thank Altair and the Fraunhofer Institute for their support with this project.

P​roblem Variables

T​he Datasets

6​ datasets have been provided. The first 5 datasets are relatively simple and vary between one and three parameters.

The 6th dataset represents a significantly more complicated problem where 7 parameters are varied. T​he variables in the 6th dataset have been selected using latin hypercube sampling approach. Users are encouraged to parse the dataset for outliers.

Dataset 4 Details