Metadata-Version: 2.1
Name: mfpml
Version: 1.0.0
Summary: Probabilistic machine learning methods
Home-page: https://github.com/JiaxiangYi96/mfpml
Author: Jiaxiang Yi
Author-email: J.Yi@tudelft.nl
License: MIT
Keywords: multi-fidelity machine learning,Bayesian Optimization
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Requires-Python: <3.12,>=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib >=3.7.2
Requires-Dist: numpy >=1.25.1
Requires-Dist: scipy >=1.11.1
Requires-Dist: pandas >=2.0.3
Requires-Dist: scikit-learn >=1.3.0

<p align="center">
    <img src="docs/source/figures/logo.png" width="70%" align="center">
</p>

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[**Documentation**](https://jiaxiangyi96.github.io/mfpml/)
| [**Installation**](https://jiaxiangyi96.github.io/mfpml/get_started.html)
| [**GitHub**](https://github.com/JiaxiangYi96/mfpml)
| [**Tutorials**](https://github.com/JiaxiangYi96/mfpml/tree/main/tutorials)

## Summary

This repository aims to provide a package for multi-fidelity probabilistic machine learning. The package is developed by Jiaxiang Yi and Ji Cheng based on their learning curve on multi-fidelity probabilistic machine learning, and multi-fidelity Bayesian optimization, and multi-fidelity reliability analysis.

Overall, this `mfpml` package has two main goals, the first one is to provide basic code on implement typical methods in modeling, optimization, and reliability analysis field. Then, based on the basic code, we also provide some advanced methods based on our publications.

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(1) Basic methods

**Models**

- Kriging model
- Multi-fidelity Kriging model

**Optimizations**

- Evolutionary algorithms
- Single fidelity Bayesian optimization
- Multi-fidelity Bayesian optimization

**Reliability analysis**

- Active learning reliability analysis
- Multi-fidelity reliability analysis

(2) Advanced methods

For the advanced methods, we will provide code based on our publications.
please check out those papers:

- Jiang, Ping, et al. "Variable-fidelity lower confidence bounding approach for engineering optimization problems with expensive simulations." AIAA Journal 57.12 (2019): 5416-5430.
- Cheng, Ji, Qiao Lin, and Jiaxiang Yi. "An enhanced variable-fidelity optimization approach for constrained optimization problems and its parallelization." Structural and Multidisciplinary Optimization 65.7 (2022): 188.
- Yi, Jiaxiang, et al. "Efficient adaptive Kriging-based reliability analysis combining new learning function and error-based stopping criterion." Structural and Multidisciplinary Optimization 62 (2020): 2517-2536.
- Yi, Jiaxiang, et al. "An active-learning method based on multi-fidelity Kriging model for structural reliability analysis." Structural and Multidisciplinary Optimization 63 (2021): 173-195.
- Yi, Jiaxiang, Yuansheng Cheng, and Jun Liu. "A novel fidelity selection strategy-guided multifidelity kriging algorithm for structural reliability analysis." Reliability Engineering & System Safety 219 (2022): 108247.

## Authorship

**Authors**:

- Jiaxiang Yi(yagafighting@gmail.com)[1]
- Ji Cheng (jicheng9617@gmail.com)[2]

**Authors affiliation:**

- [1] Delft University of Technology

- [2] City University of Hong Kong

## Community Support

If you find any issues, bugs or problems with this package, you can raise an issue
on the github page, or contact the authors directly.

## License

Copyright 2023, Jiaxiang Yi and Ji Cheng

All rights reserved.
