Computer Methods in Applied Mechanics and Engineering
 2024
DOI: 10.1016/j.cma.2024.117194
|View full text |Cite
|
Sign up to set email alerts
|

Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling

Nicholas Galioto,
Harsh Sharma,
Boris Kramer
et al.
Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 43 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?

scite logo

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Product
Browser ExtensionAssistant by sciteCitation Statement SearchReference CheckVisualizationsDashboardsExplore JournalsExplore OrganizationsExplore FundersEmbedding BadgeEmbedding Citation SearchPricing
Resources
BlogHelp & FAQAccessibility StatementAPI TermsFor Universities & GovernmentsFor ResearchersFor PublishersFor Corporate, Pharma & EnterpriseAuthor MarketingBecome an AffiliateGet an organization trial or quotescite Data & Services
About
News & PressCareersRead our PaperCoverage

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

BlogTerms and ConditionsAPI TermsPrivacy PolicyContactCookie PreferencesDo Not Sell or Share My Personal Information

Copyright © 2024 scite LLC. All rights reserved.

Made with 💙 for researchers

Part of the Research Solutions Family.