Optimization extension package for the framework for data-driven design & analysis of structures and materials
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Welcome to f3dasm_optimize, an optimization extension Python package for data-driven design and analysis of structures and materials.
- Current created and developer: M.P. van der Schelling (M.P.vanderSchelling@tudelft.nl)
The Bessa research group at TU Delft is small... At the moment, we have limited availability to help future users/developers adapting the code to new problems, but we will do our best to help!
The best way to get started is to follow the installation instructions of the f3dasm package.
If you use or edit our work, please cite at least one of the appropriate references:
[1] Bessa, M. A., Bostanabad, R., Liu, Z., Hu, A., Apley, D. W., Brinson, C., Chen, W., & Liu, W. K. (2017). A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality. Computer Methods in Applied Mechanics and Engineering, 320, 633-667.
[2] Bessa, M. A., & Pellegrino, S. (2018). Design of ultra-thin shell structures in the stochastic post-buckling range using Bayesian machine learning and optimization. International Journal of Solids and Structures, 139, 174-188.
[3] Bessa, M. A., Glowacki, P., & Houlder, M. (2019). Bayesian machine learning in metamaterial design: fragile becomes super-compressible. Advanced Materials, 31(48), 1904845.
[4] Mojtaba, M., Bostanabad, R., Chen, W., Ehmann, K., Cao, J., & Bessa, M. A. (2019). Deep learning predicts path-dependent plasticity. Proceedings of the National Academy of Sciences, 116(52), 26414-26420.
If you find any issues, bugs or problems with this template, please use the GitHub issue tracker to report them.
Copyright 2023, Martin van der Schelling
All rights reserved.
This project is licensed under the BSD 3-Clause License. See LICENSE for the full license text.