View in EDS HTML Full Text PDF Full Text

Neural lumped parameter differential equations with application in friction-stir processing.

Saved in:
Bibliographic Details
Title: Neural lumped parameter differential equations with application in friction-stir processing.
Authors: Koch, James1 (AUTHOR) james.koch@pnnl.gov, Choi, WoongJo1 (AUTHOR), King, Ethan1 (AUTHOR), Garcia, David1 (AUTHOR), Das, Hrishikesh1 (AUTHOR), Wang, Tianhao1 (AUTHOR), Ross, Ken1 (AUTHOR), Kappagantula, Keerti1 (AUTHOR)
Source: Journal of Intelligent Manufacturing. Feb2025, Vol. 36 Issue 2, p1111-1121. 11p.
Subjects: Ordinary differential equations, Lumped elements, Differential equations, Machine learning, Power tools
Abstract: Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a "lumped" element representative of the physical scales of the modeled system. For systems where the definition of a lumped element or its associated physics may be unknown, modeling tasks may be restricted to full-fidelity physics simulations. In this work, we consider data-driven modeling tasks with limited point-wise measurements of otherwise continuous systems. We build upon the notion of the Universal Differential Equation (UDE) to construct data-driven models for reducing dynamics to that of a lumped parameter and inferring its properties. The flexibility of UDEs allow for composing various known physical priors suitable for application-specific modeling tasks, including lumped parameter methods. The motivating example for this work is the plunge and dwell stages for friction-stir welding; specifically, (i) mapping power input into the tool to a point-measurement of temperature and (ii) using this learned mapping for process control. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 182636359
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Neural lumped parameter differential equations with application in friction-stir processing.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Koch%2C+James%22">Koch, James</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> james.koch@pnnl.gov</i><br /><searchLink fieldCode="AR" term="%22Choi%2C+WoongJo%22">Choi, WoongJo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22King%2C+Ethan%22">King, Ethan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Garcia%2C+David%22">Garcia, David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Das%2C+Hrishikesh%22">Das, Hrishikesh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Tianhao%22">Wang, Tianhao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ross%2C+Ken%22">Ross, Ken</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kappagantula%2C+Keerti%22">Kappagantula, Keerti</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+Manufacturing%22">Journal of Intelligent Manufacturing</searchLink>. Feb2025, Vol. 36 Issue 2, p1111-1121. 11p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Ordinary+differential+equations%22">Ordinary differential equations</searchLink><br /><searchLink fieldCode="DE" term="%22Lumped+elements%22">Lumped elements</searchLink><br /><searchLink fieldCode="DE" term="%22Differential+equations%22">Differential equations</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Power+tools%22">Power tools</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a "lumped" element representative of the physical scales of the modeled system. For systems where the definition of a lumped element or its associated physics may be unknown, modeling tasks may be restricted to full-fidelity physics simulations. In this work, we consider data-driven modeling tasks with limited point-wise measurements of otherwise continuous systems. We build upon the notion of the Universal Differential Equation (UDE) to construct data-driven models for reducing dynamics to that of a lumped parameter and inferring its properties. The flexibility of UDEs allow for composing various known physical priors suitable for application-specific modeling tasks, including lumped parameter methods. The motivating example for this work is the plunge and dwell stages for friction-stir welding; specifically, (i) mapping power input into the tool to a point-measurement of temperature and (ii) using this learned mapping for process control. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=182636359
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10845-023-02271-5
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 1111
    Subjects:
      – SubjectFull: Ordinary differential equations
        Type: general
      – SubjectFull: Lumped elements
        Type: general
      – SubjectFull: Differential equations
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Power tools
        Type: general
    Titles:
      – TitleFull: Neural lumped parameter differential equations with application in friction-stir processing.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Koch, James
      – PersonEntity:
          Name:
            NameFull: Choi, WoongJo
      – PersonEntity:
          Name:
            NameFull: King, Ethan
      – PersonEntity:
          Name:
            NameFull: Garcia, David
      – PersonEntity:
          Name:
            NameFull: Das, Hrishikesh
      – PersonEntity:
          Name:
            NameFull: Wang, Tianhao
      – PersonEntity:
          Name:
            NameFull: Ross, Ken
      – PersonEntity:
          Name:
            NameFull: Kappagantula, Keerti
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 09565515
          Numbering:
            – Type: volume
              Value: 36
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal of Intelligent Manufacturing
              Type: main
ResultId 1