@article {, title = {Optimization under Uncertainty of a Chain of Nonlinear Resonators using a Field Representation}, journal = {Applied Mathematical Modelling}, volume = {96}, year = {2021}, pages = {779-795}, doi = {10.1016/j.apm.2021.03.033}, url = {https://www.sciencedirect.com/science/article/pii/S0307904X21001645}, author = {Seyed Saeed Ahmadisoleymani and Samy Missoum} } @conference {ahmadisoleymani2018optimization, title = {Optimization of a Chain of Nonlinear Resonators for Vibration Mitigation}, booktitle = {AIAA Multidisciplinary Analysis and Optimization Conference}, year = {2018}, month = {06/2018}, publisher = {American Institute of Aeronautics and Astronautics}, organization = {American Institute of Aeronautics and Astronautics}, address = {Atlanta, GA}, doi = {10.2514/6.2018-3105}, url = {https://doi.org/10.2514/6.2018-3105}, author = {Ahmadisoleymani, Seyed Saeed and Missoum, Samy} } @conference {pidaparthi2018optimization, title = {Optimization of Nonlinear Energy Sinks for the Mitigation of Limit Cycle Oscillations}, booktitle = {AIAA Multidisciplinary Analysis and Optimization Conference}, year = {2018}, month = {06/2018}, publisher = {American Institute of Aeronautics and Astronautics}, organization = {American Institute of Aeronautics and Astronautics}, address = {Atlanta, GA}, author = {Pidaparthi, Bharath and Missoum, Samy} } @article {boroson2017optimization, title = {Optimization Under Uncertainty of Parallel Nonlinear Energy Sinks}, journal = {Journal of Sound and Vibration}, volume = {394}, year = {2017}, month = {04/2017}, pages = {451-464}, abstract = {

Nonlinear Energy Sinks (NESs) are a promising technique for passively reducing the amplitude of vibrations. Through nonlinear stiffness properties, a NES is able to passively and irreversibly absorb energy. Unlike the traditional Tuned Mass Damper (TMD), NESs do not require a specific tuning and absorb energy over a wider range of frequencies. Nevertheless, they are still only efficient over a limited range of excitations. In order to mitigate this limitation and maximize the efficiency range, this work investigates the optimization of multiple NESs configured in parallel. It is well known that the efficiency of a NES is extremely sensitive to small perturbations in loading conditions or design parameters. In fact, the efficiency of a NES has been shown to be nearly discontinuous in the neighborhood of its activation threshold. For this reason, uncertainties must be taken into account in the design optimization of NESs. In addition, the discontinuities require a specific treatment during the optimization process. In this work, the objective of the optimization is to maximize the expected value of the efficiency of NESs in parallel. The optimization algorithm is able to tackle design variables with uncertainty (e.g., nonlinear stiffness coefficients) as well as aleatory variables such as the initial velocity of the main system. The optimal design of several parallel NES configurations for maximum mean efficiency is investigated. Specifically, NES nonlinear stiffness properties, considered random design variables, are optimized for cases with 1, 2, 3, 4, 5, and 10 NESs in parallel. The distributions of efficiency for the optimal parallel configurations are compared to distributions of efficiencies of non-optimized NESs. It is observed that the optimization enables a sharp increase in the mean value of efficiency while reducing the corresponding variance, thus leading to more robust NES designs.

}, keywords = {Parallel nonlinear energy sinks; Optimization under uncertainty; Activation threshold}, doi = {http://dx.doi.org/10.1016/j.jsv.2016.12.043}, url = {http://www.sciencedirect.com/science/article/pii/S0022460X16308045}, author = {Ethan Boroson and Samy Missoum and Pierre-Olivier Mattei and Christophe Vergez} } @conference {boroson2016optimization, title = {Optimization Under Uncertainty of Parallel Nonlinear Energy Sinks}, booktitle = {57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference}, year = {2016}, address = {San Diego, CA}, doi = {10.2514/6.2016-1421 }, author = {Boroson, Ethan and Missoum, Samy} } @article {jiang2014optimal, title = {Optimal SVM Parameter Selection for Non-separable and Unbalanced Datasets}, journal = {Structural and Multidisciplinary Optimization}, volume = {50}, year = {2014}, month = {10/2014}, pages = {523-535}, abstract = {

This article presents a study of three validation metrics used for the selection of optimal parameters of a support vector machine (SVM) classifier in the case of nonseparable and unbalanced datasets. This situation is often encountered when the data is obtained experimentally or clinically. The three metrics selected in this work are the area under the ROC curve (AUC), accuracy, and balanced accuracy. These validation metrics are tested using computational data only, which enables the creation of fully separable sets of data. This way, non-separable datasets, representative of a real-world problem, can be created by projection onto a lower dimensional sub-space. The knowledge of the separable dataset, unknown in real-world problems, provides a reference to compare the three validation metrics using a quantity referred to as the {\textquotedblleft}weighted likelihood{\textquotedblright}. As an application example, the study investigates a classification model for hip fracture prediction. The data is obtained from a parameterized finite element model of a femur. The performance of the various validation metrics is studied for several levels of separability, ratios of unbalance, and training set sizes.

}, keywords = {Cross validation, Non-separable and unbalanced datasets, Support vector machines, Validation metrics}, doi = {10.1007/s00158-014-1105-z}, url = {http://codes.arizona.edu/sites/default/files/pdf/Jiang2014.pdf}, author = {Jiang, Peng and Missoum, Samy and Chen, Zhao} } @conference {boroson2014optimization, title = {Optimization Under Uncertainty of Nonlinear Energy Sinks}, booktitle = {Proceedings of the ASME International Design and Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 8: 26th Conference on Mechanical Vibration and Noise}, year = {2014}, publisher = {American Society of Mechanical Engineers}, organization = {American Society of Mechanical Engineers}, address = {Buffalo, NY}, doi = {10.1115/DETC2014-34238}, url = {http://codes.arizona.edu/sites/default/files/pdf/Boroson2014.pdf}, author = {Boroson, Ethan and Missoum, Samy and Mattei, Pierre-Olivier and Vergez, Christophe} } @conference {jiang2013optimal, title = {Optimal Parameter Selection of an SVM Model: Application to Hip Fracture Risk Prediction}, booktitle = {AIAA-2013-1935. Proceedings of the 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Boston, MA, April 8-11}, year = {2013}, doi = {10.2514/6.2013-1935}, url = {http://codes.arizona.edu/sites/default/files/pdf/Jiang2013a.pdf}, author = {Jiang, Peng and Missoum, Samy and Hu, Chengcheng and Chen, Zhao} }