The solution of time-dependent ordinary differential equations using neural networks : collocation polynomial neural forms and adaptive neural domain refinement
- Weitere Titel
-
Die Lösung von zeitabhängigen gewöhnlichen Differentialgleichungen mit Neuronalen Netzen : Neuronale Kollokationspolynome und adaptive Neuronale Gebietsverfeinerung
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
Cottbus, BTU Cottbus - Senftenberg, Dissertation, 2022
- Schlagwort
-
Neuronales Netz
Radiale Basisfunktion
Numerisches Verfahren
Differentialgleichung
Kollokation
Numerisches Verfahren
Polynom
Differential equations
Collocation methods
- Ereignis
-
Veröffentlichung
- (wo)
-
Cottbus
- (wer)
-
BTU Cottbus - Senftenberg
- (wann)
-
2023
- Urheber
-
Schneidereit, Toni
- Beteiligte Personen und Organisationen
-
Breuß, Michael
Hartmann, Carsten
Lagaris, Isaac
- DOI
-
10.26127/BTUOpen-6389
- URN
-
urn:nbn:de:kobv:co1-opus4-63896
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 14.08.2025, 08:46 UTC
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
- Schneidereit, Toni
- Breuß, Michael
- Hartmann, Carsten
- Lagaris, Isaac
- BTU Cottbus - Senftenberg
Entstanden
- 2023
Ähnliche Objekte (12)
Adaptive neural-domain refinement for solving time-dependent differential equations
nmODE: neural memory ordinary differential equation
Local parameter identification with neural ordinary differential equations
Improving Neural Network Verification through Spurious Region Guided Refinement
Collocation polynomial neural forms and domain fragmentation for solving initial value problems
Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations
Why neural networks? History of neural networks
Explanations for neural networks by neural networks
Probabilistic neural computation and neural simulation technology
Neural development
Neural Networks
Neural programming
Adaptive neural-domain refinement for solving time-dependent differential equations
nmODE: neural memory ordinary differential equation
Local parameter identification with neural ordinary differential equations
Improving Neural Network Verification through Spurious Region Guided Refinement
Collocation polynomial neural forms and domain fragmentation for solving initial value problems
Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations
Why neural networks? History of neural networks
Explanations for neural networks by neural networks
Probabilistic neural computation and neural simulation technology
Neural development
Neural Networks
Neural programming
Adaptive neural-domain refinement for solving time-dependent differential equations
nmODE: neural memory ordinary differential equation
Local parameter identification with neural ordinary differential equations
Improving Neural Network Verification through Spurious Region Guided Refinement
Collocation polynomial neural forms and domain fragmentation for solving initial value problems
Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations
Why neural networks? History of neural networks
Explanations for neural networks by neural networks
Probabilistic neural computation and neural simulation technology
Neural development
Neural Networks