Robust MDO and advanced UQ with GEMSEO.
Project description
gemseo-umdo
Overview
gemseo-umdo
is a plugin of the library GEMSEO,
dedicated to multidisciplinary optimization (MDO) under uncertainty.
MDO under uncertainty
The main goal of gemseo-umdo
is to extend GEMSEO
to MDO under uncertainty.
Given a collection of disciplines, we are interested in solving a problem like
$$ \begin{align} &\underset{x\in\mathcal{X}}{\operatorname{minimize}}& & \mathbb{E}[f(x,U)]+\kappa\times\mathbb{S}[f(x,U)] \ &\operatorname{subject;to} & &\mathbb{P}[g(x,U)\geq 0] \leq \varepsilon \end{align} $$
by selecting an MDO formulation to handle the multidisciplinary coupling and an estimation technique to approximate the statistics.
Statistics
gemseo-umdo
also proposes advanced techniques
for uncertainty quantification and management (UQ&M).
In presence of multilevel simulators,
multilevel Monte Carlo (MLMC) sampling can reduce
the variance of the statistics estimators.
Another variance reduction technique
consists of using the outputs of surrogate models
as control variates,
even moderately correlated with the original models.
Visualization
A third facet of gemseo-umdo
is the visualization toolbox
to display the propagation of the uncertainties
through a multidisciplinary system
as well as the interaction between the uncertain input variables.
Installation
Install the latest version with pip install gemseo-umdo
.
See pip for more information.
Bugs and questions
Please use the gitlab issue tracker to submit bugs or questions.
Contributing
See the contributing section of GEMSEO.
Contributors
- Antoine Dechaume
- Matthias De Lozzo
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
File details
Details for the file gemseo_umdo-3.0.0-py3-none-any.whl
.
File metadata
- Download URL: gemseo_umdo-3.0.0-py3-none-any.whl
- Upload date:
- Size: 167.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7497010b204c60470ea79d0bfb58ba3ad10f861ab1d7ed6387fde01e2ea7fd78 |
|
MD5 | 1f9dbe854ff9cc657723bf15b71bf4c4 |
|
BLAKE2b-256 | a06909b102f5d593d4901973a3c9ea35d6d7a0052b9e1deaf36749eada891a1e |