Prazo para submissão: November 30, 2023 This special issue aims at bringing together articles that discuss recent advances in machine learning and inverse problems. Machine Learning is a subset of Artificial Intelligence focusing on computers’ ability to learn from data and to imitate intelligence human behaviour. A typical inverse problem seeks to find a mathematical model that admits given observational data as an approximate solution. Recent contributions in these areas aim at exploring potential synergies between their two different domains of research. From one hand, in fact, machine learning algorithms can leverage large collections of training data to directly compute regularized reconstructions and estimate unknown parameters. From the other hand, machine learning algorithms can benefit from the vast inverse problem literature and the existing contributions to the theory of inverse problems, and they can be used to simulate boundary value data when they are missing.
Data de Notificação: 04/04/2025
Editora: Springer
Revista: Optimization and Engineering
Link: https://link.springer.com/journal/11081/updates/23782176
Detalhes:
Machine Learning and Inverse Problems: Call For Papers D. Auroux (Universite’ Cote d’Azur, France) V. Kovanis (Virginia Tech, USA) H. Kunze (University of Guelph, Canada) D. La Torre (SKEMA Business School, France)