BO-HBM-ex

BO-HBM-ex

Description

The Python's tool BO-HBM-ex shows an example of the application of Bayesian Optimization [1] on a Duffing problem [2]. The Duffing's oscillator problem is here solved using Harmonic Balance Method and a continuation technique.

Duffing oscillator scheme.

The optimization problem can be written:

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Find $(k_{nl}^*,\xi^*)$ such as

$(k_{nl}^*,\xi^*)=\underset{(k_{nl},\xi)\in\mathcal{D}}{\arg\min}\,\underset{\omega\in[\omega_l,\omega_u]}{\max} \ddot{q}_{\mathrm{RMS}}(k_{nl},\xi,\omega)$

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Installation

Get the source code from this repository. The example can be run after installation of dependencies with pip install -r requirements.txt.

Usage

The Python's scripts can be run only with Python 3.

Run solver of Duffing oscillator

The file respDuffing.py provides frequencies responses for acceleration on PDF pictures for $\xi=\{0.15, 0.3, 0.5, 1.\}$ ($[\mathrm{kg}/\mathrm{s}]$) and $k_{nl}=\{0.25,0.5,0.75,...,2\}$ ($[\mathrm{N}/\mathrm{m}^{-3}]$) . Pictures are stored in ParamDuffing folder.

Following pictures show $\ddot{q}_ {\mathrm{RMS}}$ for a few values of $k_ {nl}$ for $\xi=0.15\,\mathrm{kg}/\mathrm{s}$ (a), $\xi=0.3\,\mathrm{kg}/\mathrm{s}$ (b), $\xi=0.5\,\mathrm{kg}/\mathrm{s}$ (c) and $\xi=1\,\mathrm{kg}/\mathrm{s}$ (d).

(a)	(b)
(c)	(d)

Animations of $q_ {\mathrm{RMS}}$ (a) and $\ddot{q}_ {\mathrm{RMS}}$ (b) of large set of values of $k_{nl}$ for $\xi=0.15\,\mathrm{kg}/\mathrm{s}$:

(a)	(b)

Run Bayesian Optimization on Duffing oscillator

The file OptiExp.py generates data of Bayesian Optimization's iterations. These data will be available on the directory ExpOptimDuffing which contains data for sample sets containing 10, 20 and 25 samples. Results are provided along BO iterations on CSV files and acquisition and objective functions are plotted in 2D and 3D.

The following pictures show the evolution of the acquisition and objective functions along BO's iterations applied on the Optimization of the Duffing Oscillator (minimization of the maximum of the acceleration along the frequency bandwidth $[0,2.5]$ ($\mathrm{rad}\cdot\mathrm{s}^{-1}$)). The initial sampling obtained with LHS contains 10 sample points.

Acquisition function	Objective function

Versions

The code has been executed without any issues with the following versions of Python and libraries:

- Python 3.10.9
- numpy 1.26.2
- matplotlib 3.8.2
- pandas 2.1.3
- torch 2.1.1
- botorch 0.9.4
- gpytorch 1.11
- pydoe 0.3.8

License

MIT License

Copyright (c) 2023 - Quentin Ragueneau (quentin.ragueneau@ingeliance.com)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

References

[1] D. R. Jones, M. Schonlau, and W. J. Welch. Efficient Global Optimization of Expensive Black-Box Functions. Journal of Global Optimization, 13(4):455–492, Dec. 1998.

[2] B. Balaram, M. D. Narayanan, and P. K. Rajendrakumar. Optimal design of multi-parametric nonlinear systems using a parametric continuation based Genetic Algorithm approach. Nonlinear Dynamics, 67(4):2759–2777, Mar. 2012.