Adaptive design optimization using classifiers based machine learning paradigm

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

Download Full-text

Judgement of valence of musical sounds by hand and by heart, a machine learning paradigm for reading the heart

Heliyon ◽

10.1016/j.heliyon.2021.e07565 ◽

2021 ◽

pp. e07565

Author(s):

Ennio Idrobo-Ávila ◽

Humberto Loaiza-Correa ◽

Flavio Muñoz-Bolaños ◽

Leon van Noorden ◽

Rubiel Vargas-Cañas

Keyword(s):

Machine Learning ◽

Learning Paradigm ◽

Musical Sounds

Download Full-text

Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2016.11.011 ◽

2017 ◽

Vol 80 ◽

pp. 77-96 ◽

Cited By ~ 21

Author(s):

Tadashi Araki ◽

Pankaj K. Jain ◽

Harman S. Suri ◽

Narendra D. Londhe ◽

Nobutaka Ikeda ◽

...

Keyword(s):

Machine Learning ◽

Risk Stratification ◽

Stroke Risk ◽

Plaque Morphology ◽

Learning Paradigm ◽

Carotid Wall

Download Full-text

An Automated Machine Learning-Genetic Algorithm Framework With Active Learning for Design Optimization

Journal of Energy Resources Technology ◽

10.1115/1.4050489 ◽

2021 ◽

Vol 143 (8) ◽

Author(s):

Opeoluwa Owoyele ◽

Pinaki Pal ◽

Alvaro Vidal Torreira

Keyword(s):

Machine Learning ◽

Active Learning ◽

Design Optimization ◽

Fuel Injection ◽

A Priori ◽

Computational Cost ◽

Learning Approach ◽

Data Generation ◽

Compression Ignition Engine ◽

Hyperparameter Selection

AbstractThe use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

Download Full-text