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Call for abstracts
This workshop 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 there 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.
Both these domains are of great interest in many application areas,
including biomedical engineering and imaging, remote sensing and seismic
imaging, astronomy, oceanography, atmospheric sciences and meteorology,
chemical engineering and material sciences, computer vision and image
processing, ecology, economics, environmental systems, physical systems.
The possibility of integrating them can generate more precise estimation
and allow to estimate unknown parameters in more complex environments.
All abstracts should consider aspects of
numerical analysis, mathematical modelling, and computational methods.
This call for abstracts invites contributions from emerging areas such as
quantum inverse problems and quantum machine learning.
Potential topics
include, but are not limited to, the following:
 Deep Learning Algorithms
 Inverse Problems Techniques
 Inverse Problems for Ordinary and Differential Equations
 Optimization Methods in Inverse Problems and Machine Learning
 Machine Learning
 Neural Networks
 Neural Differential Equations
 Quantum Inverse Problems
 Quantum Machine Learning
 Shape Optimization
 Inverse Optimization
 Image Analysis
 Regularization Techniques
People can submit a onepage abstract to Didier Auroux and Davide La Torre before March 1st, 2023.
A special issue of Optimization
and Engineering (OPTE) journal will be associated with this workshop
and will host the best contributions. More details will be provided
during the workshop.
