Transport of Organic Volatiles through Paper: Physics-Informed Neural Networks for Solving Inverse and Forward Problems
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1573-1634
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Transport of Organic Volatiles through Paper: Physics-Informed Neural Networks for Solving Inverse and Forward Problems ; volume:145 ; number:3 ; day:26 ; month:10 ; year:2022 ; pages:589-612 ; date:12.2022
Transport in porous media ; 145, Heft 3 (26.10.2022), 589-612, 12.2022
- Urheber
-
Serebrennikova, Alexandra
Teubler, Raimund
Hoffellner, Lisa
Leitner, Erich
Hirn, Ulrich
Zojer, Karin
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s11242-022-01864-7
- URN
-
urn:nbn:de:101:1-2023012621063992435132
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:36 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Serebrennikova, Alexandra
- Teubler, Raimund
- Hoffellner, Lisa
- Leitner, Erich
- Hirn, Ulrich
- Zojer, Karin
- SpringerLink (Online service)
Ähnliche Objekte (12)
Physics‐Informed Inverse Design of Programmable Metasurfaces
Solving forward and inverse problems of contact mechanics using physics-informed neural networks
Solving real-world optimization tasks using physics-informed neural computing
Physics-Informed Quantum Machine Learning for Solving Partial Differential Equations
Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials
Solving crustal heat transfer for thermochronology using physics-informed neural networks
Solving crustal heat transfer for thermochronology using physics-informed neural networks
Self-adaptive physics-informed quantum machine learning for solving differential equations
Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems
POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier–Stokes equations
A physics-informed deep learning approach for solving strongly degenerate parabolic problems
Solving PDEs by variational physics-informed neural networks: an a posteriori error analysis
Physics‐Informed Inverse Design of Programmable Metasurfaces
Solving forward and inverse problems of contact mechanics using physics-informed neural networks
Solving real-world optimization tasks using physics-informed neural computing
Physics-Informed Quantum Machine Learning for Solving Partial Differential Equations
Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials
Solving crustal heat transfer for thermochronology using physics-informed neural networks
Solving crustal heat transfer for thermochronology using physics-informed neural networks
Self-adaptive physics-informed quantum machine learning for solving differential equations
Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems
POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier–Stokes equations
A physics-informed deep learning approach for solving strongly degenerate parabolic problems
Solving PDEs by variational physics-informed neural networks: an a posteriori error analysis
Physics‐Informed Inverse Design of Programmable Metasurfaces
Solving forward and inverse problems of contact mechanics using physics-informed neural networks
Solving real-world optimization tasks using physics-informed neural computing
Physics-Informed Quantum Machine Learning for Solving Partial Differential Equations
Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials
Solving crustal heat transfer for thermochronology using physics-informed neural networks
Solving crustal heat transfer for thermochronology using physics-informed neural networks
Self-adaptive physics-informed quantum machine learning for solving differential equations
Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems
POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier–Stokes equations
A physics-informed deep learning approach for solving strongly degenerate parabolic problems