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Asymptotic analysis of interacting particle systems and random matrix theory


In recent years random matrices have found applications in a vast number of different areas of science, such as telecommunications, high energy physics, number theory, machine learning, big data, dynamical systems, differential equations, computer science, among several others. The connection with interacting particle systems becomes natural when one asks questions about eigenvalues of large random matrices. Typically, such random eigenvalues interact with one another in a repulsive way, mimicking the interactions of several different and seemingly unrelated models from both equilibrium and non-equilibrium systems. Thus, one of the fundamental questions is to understand how the eigenvalues of a given large random matrix behave in different scaling regimes. This project proposes to explore several different facets of eigenvalues of large random matrices and other interacting particle systems. We will study how the thermodynamic limit of different interacting particle systems can be understood in terms of equilibrium problems on the plane, and use this information to carry out the asymptotic analysis of the models of interest, also expecting to unravel novel striking connections with integrable systems. (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
CELSUS, ANDREW F.; SILVA, GUILHERME L. F. Supercritical regime for the kissing polynomials. Journal of Approximation Theory, v. 255, JUL 2020. Web of Science Citations: 0.
SILVA, GUILHERME L. F.; ZHANG, LUN. Large n Limit for the Product of Two Coupled Random Matrices. Communications in Mathematical Physics, v. 377, n. 3 JUN 2020. Web of Science Citations: 0.

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