Cari colleghi,
Ecco l'annuncio di una conferenza al CIRM di Marsiglia che potrebbe interessarvi.
Giovanni
Dear All,
We are happy to announce the upcoming conference
“Schrödinger Problem and Mean-field PDE Systems: Computational and Theoretical Advances”
taking place at CIRM in Luminy, France from Nov. 15 to Nov. 19 2021.
The webpage of the event is publicly available at
https://conferences.cirm-math.fr/2413.html
and you can find below a brief description of the themes of the conference.
Registration is open until July 1st. At the moment we aim for a hybrid (online+in person) conference, but this could change depending on the development of the pandemic.
Limited funding is available for junior participants or participants who cannot support themselves. Moreover, there are also some free slots for contributed talks.
We hope to see you soon, in whichever form.
Best regards, the organizers.
Julio Backhoff, Guillaume Carlier, Giovanni Conforti, Ivan Gentil, Daniela Tonon.
--------------------------------------------------------------------------------------------------------------------------------------------
“Schrödinger Problem and Mean-field PDE Systems: Computational and Theoretical Advances”
Monge’s question of how to optimally move sand-pile stands at the origin of the modern theory of optimal transport. The development of this theory over the last decades led to impressive advances in analysis and geometry, reaching out to applied fields such as economics and machine learning. One and a half century after Monge, Schrödinger asked: “What is the most likely evolution of a cloud of random particles conditionally on the observation of their initial and final configurations? "This question, going under the name of Schödinger Problem, initiated a research line that has grown enormously in the last years since it was understood that Schrödinger’s and Monge’s questions are the same when the fluctuations of the random particles are very small. This discovery offered a natural ground for the theories of optimal transport and large deviations to meet and thrive together. At the same time, it also inspired the use of entropic regularization techniques in machine learning and numerics for PDEs, achieving major computational advantages. Asking Schrödinger’s question for strategic particles is the gateway to connect the Schrödinger problem and large deviations with the theory of mean field stochastic control and planning mean field games. This brings to light new mathematical questions. Can entropic regularization speed up the computation of Nash equilibria? Are there new functional inequalities that capture the ergodic behaviour of mean field games? Bringing together researchers from the areas of probability, optimal transport, statistical machine learning and mean field games, this conference aims to strengthen the interplay between the Schrödinger problem and mean field systems, and facilitate the birth of novel methodologies and results.