Cargando…
A General Deep Learning Method for Computing Molecular Parameters of a Viscoelastic Constitutive Model by Solving an Inverse Problem
Prediction of molecular parameters and material functions from the macroscopic viscoelastic properties of complex fluids are of great significance for molecular and formulation design in fundamental research as well as various industrial applications. A general learning method for computing molecula...
Autores principales: | Ye, Minghui, Fan, Yuan-Qi, Yuan, Xue-Feng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490131/ https://www.ncbi.nlm.nih.gov/pubmed/37688218 http://dx.doi.org/10.3390/polym15173592 |
Ejemplares similares
-
Design of Loss Functions for Solving Inverse Problems Using Deep Learning
por: Rivera, Jon Ander, et al.
Publicado: (2020) -
Ambiguity in Solving Imaging Inverse Problems with Deep-Learning-Based Operators
por: Evangelista, Davide, et al.
Publicado: (2023) -
Impact of the Endocardium in a Parameter Optimization to Solve the Inverse Problem of Electrocardiography
por: Ravon, Gwladys, et al.
Publicado: (2019) -
Discretization of Learned NETT Regularization for Solving Inverse Problems
por: Antholzer, Stephan, et al.
Publicado: (2021) -
Study on Parameter Optimization for Support Vector Regression in Solving the Inverse ECG Problem
por: Jiang, Mingfeng, et al.
Publicado: (2013)