Metadata-Version: 2.1
Name: duqo
Version: 0.0a1
Summary: A framework for (D)esign (U)ncertainty (Q)uantification and (O)ptimization
Home-page: https://github.com/canbooo/duqo
Author: Can Bogoclu
Author-email: can.bogoclu@gmail.com
License: BSD 3-Clause License
Project-URL: Bug Tracker, https://github.com/canbooo/duqo/issues
Description: # DUQO: *D*esign *U*ncertainty *Q*uantification and *O*ptimization framework
        The repo is under ongoing development. Breaking changes may occur in next versions.
        
        Current stable version can be installed using pip
        `pip install duqo`
        
        Alternatively, clone the master branch and use `pip install -e .`
        
        See [examples](https://github.com/canbooo/duqo/tree/master/examples) for example usage. Documentation is on its way.
        
        ## Reliability Analysis and Reliability-based Robust Design Optimization
        Generally, given one or more limit state functions of form
        > <img src="https://render.githubusercontent.com/render/math?math=%5Ccolor%7Bred%7D%20g(%5Cmathbf%7Bx%7D)%3A%20%5Cmathbb%7BR%7D%5En%20%5Crightarrow%20%5Cmathbb%7BR%7D">
        as well as  the input distributions 
        > <img src="https://render.githubusercontent.com/render/math?math=%5Ccolor%7Bred%7D%20%5Cmathbf%7BX%7D%20%5Csim%20F_%7B%5Cmathbf%7BX%7D%7D(%5Ccdot%2C%20%5Cboldsymbol%7B%5Ctheta%7D_%7B%5Cmathbf%7BX%7D%7D)">
        as parametrized by <img src="https://render.githubusercontent.com/render/math?math=%5Ccolor%7Bred%7D%20%5Cboldsymbol%7B%5Ctheta%7D_%7B%5Cmathbf%7BX%7D%7D">, 
        uncertainty quantification, i.e. reliability-analysis, seeks to compute the probability of failure
        > <img src="https://render.githubusercontent.com/render/math?math=%5Ccolor%7Bred%7D%20P(%5Cmathcal%7BF%7D)%20%3D%20P(g(%5Cmathbf%7BX%7D)%20%3C%200)">
        
        To solve the uncertainty optimization, i.e. reliability-based robust design optimization, problem, the evaluation of <img src="https://render.githubusercontent.com/render/math?math=%5Ccolor%7Bred%7D%20P(%5Cmathcal%7BF%7D)">
        as well as the expectations and variances of the objective functions 
        > <img src="https://render.githubusercontent.com/render/math?math=%5Ccolor%7Bred%7D%20f(%5Cmathbf%7Bx%7D)%3A%20%5Cmathbb%7BR%7D%5En%20%5Crightarrow%20%5Cmathbb%7BR%7D">
        and possible deterministic constraints
        > <img src="https://render.githubusercontent.com/render/math?math=%5Ccolor%7Bred%7D%20c(%5Cmathbf%7Bx%7D)%3A%20%5Cmathbb%7BR%7D%5En%20%5Crightarrow%20%5Cmathbb%7BR%7D">
        with respect to the distribution parameters <img src="https://render.githubusercontent.com/render/math?math=%5Ccolor%7Bred%7D%20%5Ctheta_%7B%5Cmathbf%7BX%7D%7D">
        is required. Besides the input distributions, `duqo` takes the objectives <img src="https://render.githubusercontent.com/render/math?math=%5Ccolor%7Bred%7D%20f_i(%5Ccdot)">,
        the limit states <img src="https://render.githubusercontent.com/render/math?math=%5Ccolor%7Bred%7D%20g_j(%5Ccdot)"> and the constraints 
        <img src="https://render.githubusercontent.com/render/math?math=%5Ccolor%7Bred%7D%20c_k(%5Ccdot)">
        as input and wraps them with `problem.obj_con` to be used by a generic gradient-free optimization algorithm.
        
        ## Citation
        If this repo helped you, I would appreciate citations:
        
        > C. Bogoclu, T. Nestorović, D. Roos; *Local Latin Hypercube Refinement for Multi-objective Design Uncertainty Optimization*,
        Applied Soft Computing (2021)
        
        ## Contribution
        Contributions welcome as there is a long road ahead to make this research code to a usable one.
        
        
Platform: any
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: BSD License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
