f3dasm 1.2.0

f3dasm - Framework for Data-driven development and Analysis of Structures and Materials

f3dasm

Framework for data-driven design & analysis of structures and materials

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Welcome to f3dasm, a Python package for data-driven design and analysis of structures and materials.

• Created by M.A. Bessa ([e-mail]) in September 2016
• Current developer and maintainer: M.P. van der Schelling ([e-mail])

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!

Referencing

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.