guest ::
A physics-informed neural network (PINN) framework for inverse mixed contact-traction tire-load reconstruction and finite-layer pavement response
In
Computers and geotechnics 202, Seiten/Artikel-Nr.:108632
2027 & 2026
Received 12 June 2026, Revised 4 September 2026, Accepted 5 September 2026, Available online 12 September 2026, Version of Record 12 September 2026
Online
DOI: 10.18154/RWTH-2026-09126
DOI: 10.1016/j.compgeo.2026.108632
URL: https://publications.rwth-aachen.de/record/1042107/files/1042107.pdf
Einrichtungen
Thematische Einordnung (Klassifikation)
DDC: 690
OpenAccess: 
PDF
Dokumenttyp
Journal Article
Format
online, print
Sprache
English
Anmerkung
Peer reviewed article
Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-105050177704
WOS Core Collection: WOS:001877555500001
Interne Identnummern
RWTH-2026-09126
Datensatz-ID: 1042107
Beteiligte Länder
Germany
; 
; 
; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
|
The record appears in these collections: |
| Typ | Amount | VAT | Currency | Share | Status | Cost centre |
| APC | 2652.00 | 185.64 | EUR | 96.82 % | (DEAL) | 021000-300000 |
| Other | 87.00 | 6.09 | EUR | 3.18 % | (DEAL) | 021000-300000 |
| Sum | 2739.00 | 191.73 | EUR | |||
| Total | 2930.73 |
PDF