Coupling 1D xRAGE simulations with machine learning for graded inner shell design optimization in double shell capsules

2021 ◽  
Vol 28 (12) ◽  
pp. 122709
Author(s):  
N. N. Vazirani ◽  
M. J. Grosskopf ◽  
D. J. Stark ◽  
P. A. Bradley ◽  
B. M. Haines ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Shell Design

scholarly journals Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

2021 ◽  
Vol 11 (4) ◽  
pp. 1627
Author(s):  
Yanbin Li ◽  
Gang Lei ◽  
Gerd Bramerdorfer ◽  
Sheng Peng ◽  
Xiaodong Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Optimization Methods ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Robust Design Optimization ◽  
Support Vector ◽  
Future Directions ◽  
Electromagnetic Devices ◽  
Recent Developments

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.

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

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.

Progress on Machine Learning Informed Design Optimization for Double Shell Capsule Graded Density Inner Layer Targets

2021 ◽  
Author(s):  
Nomita Vazirani ◽  
Michael J. Grosskopf ◽  
David J. Stark ◽  
Paul Bradley ◽  
B. Haines ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization

scholarly journals Machine Learning and Design Optimization for Molecular Biology and Beyond

2020 ◽  
Vol 25 (1) ◽  
Author(s):  
Bryan Hsu ◽  
Natasha Raut ◽  
Kaitlyn Wang ◽  
Elettra Preosti
Keyword(s):  
Machine Learning ◽  
Molecular Biology ◽  
Design Optimization

Fast and Intelligent Antenna Design Optimization using Machine Learning

2021 ◽  
Vol 35 (11) ◽  
pp. 1350-1351
Author(s):  
Gopinath Gampala ◽  
C. J. Reddy
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Design Optimization ◽  
Antenna Design ◽  
Convergence Criteria ◽  
Time Required

Traditional antenna optimization solves the modified version of the original antenna design for each iteration. Thus, the total time required to optimize a given antenna design is highly dependent on the convergence criteria of the selected algorithm and the time taken for each iteration. The use of machine learning enables the antenna designer to generate trained mathematical model that replicates the original antenna design and then apply optimization on the trained model. Use of trained model allows to run thousands of optimization iterations in a span of few seconds.

Advanced Design Optimization Technique for Torque Profile Improvement in Six-Phase PMSM Using Supervised Machine Learning for Direct-Drive EV

2019 ◽  
Vol 34 (4) ◽  
pp. 2041-2051 ◽  
Author(s):  
Himavarsha Dhulipati ◽  
Eshaan Ghosh ◽  
Shruthi Mukundan ◽  
Philip Korta ◽  
Jimi Tjong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Optimization Technique ◽  
Supervised Machine Learning ◽  
Direct Drive

Adaptive design optimization using classifiers based machine learning paradigm

1997 ◽  
Author(s):  
Sanjay Goel ◽  
Prabhat Hajela ◽  
Sanjay Goel ◽  
Prabhat Hajela
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Adaptive Design ◽  
Learning Paradigm

Machine-Learning-Based Error Detection and Design Optimization in Signal Integrity Applications

2019 ◽  
Vol 9 (9) ◽  
pp. 1712-1720 ◽  
Author(s):  
Roberto Medico ◽  
Domenico Spina ◽  
Dries Vande Ginste ◽  
Dirk Deschrijver ◽  
Tom Dhaene
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Error Detection ◽  
Signal Integrity

Clustering discretization methods for generation of material performance databases in machine learning and design optimization

2019 ◽  
Vol 64 (2) ◽  
pp. 281-305 ◽  
Author(s):  
Hengyang Li ◽  
Orion L. Kafka ◽  
Jiaying Gao ◽  
Cheng Yu ◽  
Yinghao Nie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Discretization Methods
Sign in / Sign up

Export Citation Format

Share Document