f3dasm 1.4.8

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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Docs | Installation | GitHub | PyPI

Summary

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

f3dasm introduces a general and user-friendly data-driven Python package for researchers and practitioners working on design and analysis of materials and structures. Some of the key features include:

• Modular design

  • The framework introduces flexible interfaces, allowing users to easily integrate their own models and algorithms.

• Automatic data management

  • The framework automatically manages I/O processes, saving you time and effort implementing these common procedures.

• Easy parallelization

  • The framework manages parallelization of experiments, and is compatible with both local and high-performance cluster computing.

• Built-in defaults

  • The framework includes a collection of benchmark functions, optimization algorithms and sampling strategies to get you started right away!

• Hydra integration

  • The framework is supports the hydra configuration manager, to easily manage and run experiments.

Getting started

The best way to get started is to follow the installation instructions.

Illustrative benchmarks

This package includes a collection of illustrative benchmark studies that demonstrate the capabilities of the framework. These studies are available in the /studies folder, and include the following studies:

• Benchmarking optimization algorithms against well-known benchmark functions
• 'Fragile Becomes Supercompressible' (Bessa et al. (2019))

Authorship

• Current created and developer: 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.

Community Support

If you find any issues, bugs or problems with this template, please use the GitHub issue tracker to report them.

License

Copyright 2024, Martin van der Schelling

All rights reserved.

This project is licensed under the BSD 3-Clause License. See LICENSE for the full license text.