Inverse problems and machine learning

June 29-30, 2023

Université Côte d'Azur
Parc Valrose
Nice, France

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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 one-page 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.