Minisymposium (ID: SS-AIIPMM)

Artificial Intelligence, Inverse Problems, and Mathematical Modelling

Leopoldo Bertossi (SKEMA Business School Canada, Montreal), Herb Kunze (University of Guelph) and Davide La Torre (SKEMA Business School, Universite Cote d'Azur)

A typical inverse problem seeks to find a mathematical model that admits given observational data as an approximate solution. This sort of question is of great interest in many application areas, including mathematical ecology, environmental systems, physical systems, business and economics, and image science, often appearing in the form of a parameter estimation problem. This session focuses on analysis and modelling related to such problems, with the possible added twist of seeking to incorporate an element of AI and/or machine learning (ML) in the problem or method. For example, ML algorithms can (i) leverage large collections of training data to directly compute regularized reconstructions and estimate unknown parameters, and (ii) benefit from the vast inverse problem literature and the existing contributions to the theory of inverse problems.

The talks in the session may also include aspects of numerical analysis, mathematical modeling, computational methods, and applied analysis of direct problems.

---

Please note the ID code assigned to your presentation (identical to the ID code of your accepted abstract). It is required for submitting your paper for the AMMCS-2023 Proceedings. Submission is not mandatory. All submitted papers will be refereed and only accepted papers will be published in the AMMCS-2023 Proceedings.

If you intend to submit your paper, please go to the AMMCS Proceedings Page. Follow exactly the Author Instructions accessible from that page.