Deep-learning based parameter identification enables rationalization of battery material evolution in complex electrochemical systems-Reference-Cited by-同舟云学术

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Deep-learning based parameter identification enables rationalization of battery material evolution in complex electrochemical systems

Published:2023-01 Issue: Volume:66 Page:101900
ISSN:1877-7503
Container-title:Journal of Computational Science
language:en
Short-container-title:Journal of Computational Science

Author:

Sgura Ivonne,Mainetti Luca,Negro Francesco,Quarta Maria Grazia,Bozzini Benedetto

Publisher

Elsevier BV

Subject

Modeling and Simulation,General Computer Science,Theoretical Computer Science

Reference27 articles.

1. Spatio-temporal organisation in alloy electrodeposition: a morphochemical mathematical model and its experimental validation;Bozzini;J. Solid State Electrochem,2013

2. Spatio-temporal organization in a morphochemical electrodeposition model: analysis and numerical simulation of spiral waves;Lacitignola;Acta Appl. Math.,2014

3. Spatio-temporal organization in a morphochemical electrodeposition model: hopf and turing instabilities and their interplay;Lacitignola;Eur. J. Appl. Math.,2015

4. Parameter estimation for a morphochemical reaction-diffusion model of electrochemical pattern formation;Sgura;Inverse Probl. Sci. Eng.,2019

5. An efficient and robust numerical algorithm for estimating parameters in turing systems;Garvie;J. Comput. Phys.,2010

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4. Scanning photoelectron microscopy discloses the role of Cr and Mo in the selective corrosion of hardmetal grades with Co-Ni binders;International Journal of Refractory Metals and Hard Materials;2024-01

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