Online Catalog - Item information

Philadelphia University
Library & Information Resources

Technical Services Department
Online Catalog - Item Information
Date:27/05/24

Publication type: Article

Title:

Machine Learning to Aid Tuning of Numerical Parameters in Topology Optimization

Author(s):

Lynch,Matthew E. (Author) Sarkar,Soumalya (Author) Maute,Kurt (Author)

Source Journal Info.

Title:Journal of Mechanical Design

Issue Number:2019/NOV V.141 N.11
Call No.:621.05 JMD
Location: References & Periodical Hall - 2nd floor

Physical Description:

p 1-8

Subject Area:

Engineering

Subject Terms:

Mechanical engineering Engineering design

Accession Number

178618

Abstract
Recent advances in design optimization have significant potential to improve the function of mechanical components and systems. Coupled with additive manufacturing, topology optimization is one category of numerical methods used to produce algorithmically generated optimized designs making a difference in the mechanical design of hardware currently being introduced to the market. ... Abstract URL:https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/141/11/114502/955325/Machine-Learning-to-Aid-Tuning-of-Numerical?redirectedFrom=fulltext

Philadelphia University
Library & Information Resources

Technical Services Department
Online Catalog - Item Information
Date:27/05/24

Publication type: Article

Title:

Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration

Author(s):

Sarkar,Soumalya (Author) Mondal,Sudeepta (Author)

Source Journal Info.

Title:Journal of Mechanical Design

Issue Number:2019/DEC V.141 N.12
Call No.:621.05 JMD
Location: References & Periodical Hall - 2nd floor

Physical Description:

p 1-11

Subject Area:

Engineering

Subject Terms:

Mechanical engineering Engineering design

Accession Number

178630

Abstract
This paper proposes a machine learning–based multifidelity modeling (MFM) and information-theoretic Bayesian optimization approach where the associated models can have complex discrepancies among each other. Advantages of MFM-based optimization over a single-fidelity surrogate, specifically under complex constraints, are discussed with benchmark optimization problems involving noisy data. ... Abstract URL:https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/141/12/121001/975244/Multifidelity-and-Multiscale-Bayesian-Framework?redirectedFrom=fulltext

For more info. : Tel: +962-6-4799000 Technical Services Department Ext.2377 Email: Library@philadelphia.edu.jo