Dear all, a gentle reminder of the mini-course by Michael Yampolsky. It will be also possible to follow the lectures remotely at the link given in the message below. Kind regards Stefano Marmi
Dear all,
In the next few weeks Prof. Michael Yampolsky (University of Toronto Mississauga) will give a series of 4 lectures on*“Computability and Computational Complexity Questions in Dynamics”* at the Scuola Normale Superiore in Pisa. The lectures will take place on Thursdays October 24 and 31 and November 7 and 14, at* 4:30* pm in* Aula Volterra*, Piazza dei Cavalieri 7 and will be streamed online.
Please find below the title and abstract of the first lecture (Thursday October 24) and the relative zoom link.
*October 24:* *Computability and computational complexity in dynamics: can we trust numerical predictions of limiting behavior of orbits?* The development of the modern subject of dynamical systems went hand-in-hand with numerical modeling. However, the theoretical basis of such modeling has remained largely unexplored and offers exciting challenges. What can and cannot be computed about the behavior of a dynamical system? I will give an overview of some of the recent results and directions of study.
*Zoom link* : https://us02web.zoom.us/j/87207701292?pwd=cS85dXl0WC9udDhJNmkrd3FQVDJhdz09
Below you can find titles and abstracts of the next lectures.
*October 31:* *Don’t believe your lying eyes: (non)-computability of Julia sets.* The talk will be a deep dive into the question of computability and computational complexity of Julia sets. Their fractal images are among the most-computed pictures in all of mathematics. But can we trust what these computations show? The results are surprising, and involve some beautiful complex analysis. The talk will be based on my work with M. Braverman, as well as more recent results obtained with A. Dudko, C. Rojas, and others.
*November 7:* *How to lose at Monte Carlo: computing statistical behavior of dynamical systems.* Introduced by Ulam and von Neumann in the 1940s, Monte Carlo technique remains the most ubiquitous tool for statistical modeling. Surprisingly, our recent work with C. Rojas shows that such modeling probably fails for some very simple dynamical systems. I will present this theorem and discuss the resulting mathematical challenges.
*November 14:* *Thurston versus Turing: algorithmic hardness of classifying finite dynamical systems.* Historically, the algorithmic aspects of classification problems have motivated much of the research in geometric topology (knot theory, classification of surfaces and 3-manifolds, etc). These questions are also very fruitful in dynamics. I will discuss the joint results with N. Selinger, K. Rafi and others on algorithmic decidability of Thurston equivalence and the connection with geometrization questions in dynamics.
Best wishes,
Roberto