Seminars and Colloquia by Series

Stiffness and rigidity in random dynamics

Series
CDSNS Colloquium
Time
Friday, April 8, 2022 - 13:00 for 1 hour (actually 50 minutes)
Location
Online via Zoom
Speaker
Aaron BrownNorthwestern University

Please Note: Link: https://us06web.zoom.us/j/2782194473?pwd=L1Nnc0c1SXFFYkZqSkVGUGpEd2E4dz09

Consider two volume-preserving, smooth diffeomorphisms f and g of a compact manifold M.  Define the random walk on M by selecting either f or g (i.i.d.) at each iterate.  A number of questions arise in this setting:

  1. What are the closed subsets of M invariant under both f and g?
  2. What are the stationary measures on M for the random walk.  In particular, are the stationary measures invariant under f and g?

Conjecturally, for a generic pair of f and g we should be able to answer the above.  I will describe one sufficient criteria on f and g underwhich we can give some partial answers to the above questions.  Such a criteria is expected to be generic amoung pairs of (volume-preserving) diffeomorphisms and should be able to be verified in a number of naturally occurring geometric settings where the above questions are not fully answered.  

On mix-norms and the rate of decay of correlations

Series
CDSNS Colloquium
Time
Friday, March 18, 2022 - 13:00 for 1 hour (actually 50 minutes)
Location
Online via Zoom
Speaker
Bryan OakleyUniversity of Wisconsin-Madison

Please Note: Zoom link: https://us06web.zoom.us/j/83392531099?pwd=UHh2MDFMcGErbzFtMHBZTmNZQXM0dz09

Two quantitative notions of mixing are the decay of correlations and the decay of a mix-norm --- a negative Sobolev norm --- and the intensity of mixing can be measured by the rates of decay of these quantities. From duality, correlations are uniformly dominated by a mix-norm; but can they decay asymptotically faster than the mix-norm? We answer this question by constructing an observable with correlation that comes arbitrarily close to achieving the decay rate of the mix-norm. Therefore the mix-norm is the sharpest rate of decay of correlations in both the uniform sense and the asymptotic sense. Moreover, there exists an observable with correlation that decays at the same rate as the mix-norm if and only if the rate of decay of the mix-norm is achieved by its projection onto low-frequency Fourier modes. In this case, the function being mixed is called q-recurrent; otherwise it is q-transient. We use this classification to study several examples and raise questions for future investigations. 

On Herman positive metric entropy conjecture

Series
CDSNS Colloquium
Time
Friday, March 11, 2022 - 13:00 for 1 hour (actually 50 minutes)
Location
Online via Zoom
Speaker
Dmitry TuraevImperial College

Please Note: Link: https://us06web.zoom.us/j/83392531099?pwd=UHh2MDFMcGErbzFtMHBZTmNZQXM0dz09

Consider any area-preserving map of R2 which has an elliptic periodic orbit. We show that arbitrarily close to this map (in the C-infinity topology) there exists an area-preserving map which has a "chaotic island" - an open set where every point has positive maximal Lyapunov exponent. The result implies that the naturally sound conjectures that relate the observed chaotic behavior in non-hyperbolic conservative systems with the positivity of the metric entropy need a rethinking. 

An analytic study of intermittency and multifractality through Riemann's non differentiable function

Series
CDSNS Colloquium
Time
Friday, March 4, 2022 - 13:00 for 1 hour (actually 50 minutes)
Location
Skiles 05
Speaker
Victor Vilaça Da RochaGeorgia Tech

Different ways have been introduced to define intermittency in the theory of turbulence, like for example the non-gaussianity, the lack of self-similarity or the deviation of the theory of turbulence by Kolmogorov from 1941.

The usual tool to measure intermittency is the flatness, a measure of the variation of the velocity at small scale, using structure functions in the spatial domain, or high-pass filters in the frequency domain. However, these two approaches give different results in some experiences.

The goal here is to study and compare these two methods and show that the result depends on the regularity of the studied function. For that purpose, we use Riemann's non-differentiable functions. To motivate this choice, we'll present the link between this function, the vortex filament equation, and the multifractal formalism.
This is a work in collaboration with Daniel Eceizabarrena (University of Massachusetts Amherst) and Alexandre Boritchev (University of Lyon)
 
 

Simultaneous Linearization of Diffeomorphisms of Isotropic Manifolds

Series
CDSNS Colloquium
Time
Friday, February 25, 2022 - 13:00 for 1 hour (actually 50 minutes)
Location
Online via Zoom
Speaker
Jonathan DeWittU Chicago

Please Note: Link: https://us06web.zoom.us/j/83392531099?pwd=UHh2MDFMcGErbzFtMHBZTmNZQXM0dz09

Suppose that $M$ is a closed isotropic Riemannian manifold and that $R_1,...,R_m$ generate the isometry group of $M$. Let $f_1,...,f_m$ be smooth perturbations of these isometries. We show that the $f_i$ are simultaneously conjugate to isometries if and only if their associated uniform Bernoulli random walk has all Lyapunov exponents zero. This extends a linearization result of Dolgopyat and Krikorian from $S^n$ to real, complex, and quaternionic projective spaces.

Ergodic optimization and multifractal formalism of Lyapunov exponents

Series
CDSNS Colloquium
Time
Friday, February 18, 2022 - 13:00 for 1 hour (actually 50 minutes)
Location
Zoom, see below
Speaker
Reza MohammadpourUppsala university

Please Note: https://us06web.zoom.us/j/83392531099?pwd=UHh2MDFMcGErbzFtMHBZTmNZQXM0dz09

In this talk, we discuss ergodic optimization and multifractal behavior of Lyapunov exponents for matrix cocycles. We show that the restricted variational principle holds for generic cocycles over mixing subshifts of finite type and that the Lyapunov spectrum is equal to the closure of the set where the entropy spectrum is positive for such cocycles. Moreover, we show the continuity of the lower joint spectral radius for linear cocycles under the assumption that linear cocycles satisfy a cone condition.

We consider a subadditive potential $\Phi$. We obtain that for $t \to \infty$ any accumulation point of a family of equilibrium states of $t\Phi$ is a maximizing measure and that the Lyapunov exponent and entropy of equilibrium states for $t\Phi$ converge in the limit $t\to \infty$  to the maximal Lyapunov exponent and entropy of maximizing measures. Moreover, we show that if a $SL(2, \mathbb{R})$ one-step cocycle satisfies pinching and twisting conditions and there exist strictly invariant cones whose images do not overlap on the Mather set then the Lyapunov-maximizing measures have zero entropy.

Open sets of partially hyperbolic systems having a unique SRB measure

Series
CDSNS Colloquium
Time
Friday, December 10, 2021 - 13:00 for 1 hour (actually 50 minutes)
Location
Zoom (see additional notes for link)
Speaker
Davi ObataU Chicago

Please Note: https://us06web.zoom.us/j/83392531099?pwd=UHh2MDFMcGErbzFtMHBZTmNZQXM0dz09

For a dynamical system, a physical measure is an ergodic invariant measure that captures the asymptotic statistical behavior of the orbits of a set with positive Lebesgue measure. A natural question in the theory is to know when such measures exist.

It is expected that a "typical" system with enough hyperbolicity (such as partial hyperbolicity) should have such measures. A special type of physical measure is the so-called hyperbolic SRB (Sinai-Ruelle-Bowen) measure. Since the 70`s the study of SRB measures has been a very active topic of research. 

In this talk, we will see a new example of open sets of partially hyperbolic systems with two dimensional center having a unique SRB measure.  One of the key features for these examples is a rigidity result for a special type of measure (the so-called u-Gibbs measure) which allows us to conclude the existence of the SRB measures.

A traveling wave bifurcation analysis of turbulent pipe flow

Series
CDSNS Colloquium
Time
Friday, December 3, 2021 - 13:00 for 1 hour (actually 50 minutes)
Location
Online via Zoom
Speaker
Maximilian EngelFU Berlin

Please Note: Zoom link-- https://us06web.zoom.us/j/83392531099?pwd=UHh2MDFMcGErbzFtMHBZTmNZQXM0dz09

Using techniques from dynamical systems theory, we rigorously study an experimentally validated model by [Barkley et al., Nature, 526:550-553, 2015], which describes the rise of turbulent pipe flow via a PDE system of reduced complexity. The fast evolution of turbulence is governed by reaction-diffusion dynamics coupled to the centerline velocity, which evolves with advection of Burgers' type and a slow relaminarization term. Applying to this model a spatial dynamics ansatz, we prove the existence of a heteroclinic loop between a turbulent and a laminar steady state and establish a cascade of bifurcations of traveling waves mediating the transition to turbulence, with a focus on an intermediate Reynolds number regime.

This is joint work with Björn de Rijk and Christian Kuehn.

Computer assisted proof of transverse homoclinic chaos - a look under the hood

Series
CDSNS Colloquium
Time
Friday, November 19, 2021 - 13:00 for 1 hour (actually 50 minutes)
Location
Skiles 005; streaming via Zoom available
Speaker
J.D. Mireles JamesFlorida Atlantic University

Please Note: Talk will be held in-person in Skiles 005 and streamed synchronously. Zoom link-- https://us06web.zoom.us/j/83392531099?pwd=UHh2MDFMcGErbzFtMHBZTmNZQXM0dz09

My goal is to present a computer assisted proof of a non-trivial theorem in nonlinear dynamics, in full detail.  My (quite biased) definition of non-trivial is that there should be some infinite dimensional complications.  However, since I want to go through all the details, I need these complications to be as simple as possible.  So, I'll consider the Henon map, and prove that some 1 dimensional stable and unstable manifolds attached to a hyperbolic fixed point intersect transversally.  By Smale's theorem, this implies the existence of chaotic motions.  Recall that one can prove the existence chaotic dynamics for the Henon map more or less by hand using topological methods.  Yet transverse intersection of the manifolds is a stronger statement, and moreover the method I'll discuss generalizes to much more sophisticated examples where pen-and-paper fail.

The idea of the proof is to develop a high order polynomial expansion of the stable/unstable manifolds of the fixed point, to prove an a-posteriori theorem about the convergence and truncation error bounds for this expansion, and to check the hypotheses of this theorem using the computer.  All of this relies on the parameterization method of Cabre, Fontich, and de la Llave, and on finite numerical calculations using interval arithmetic to manage the inevitable roundoff errors. Once global enough representations of the local invariant manifolds are obtained and equipped with mathematically rigorous error bounds, it is a finite dimensional problem to establish that the manifolds intersect transversally.  

When machine learning meets dynamics - a few examples

Series
CDSNS Colloquium
Time
Friday, November 12, 2021 - 13:00 for 1 hour (actually 50 minutes)
Location
Online via Zoom
Speaker
Molei TaoGeorgia Tech

Please Note: Zoom link: https://us06web.zoom.us/j/83392531099?pwd=UHh2MDFMcGErbzFtMHBZTmNZQXM0dz09

This talk will report some of our progress in showing how dynamics can be a useful mathematical tool for machine learning. Three demonstrations will be given, namely, how dynamics help design (and analyze) optimization algorithms, how dynamics help quantitatively understand nontrivial observations in deep learning practices, and how deep learning can in turn help dynamics (or more broadly put, AI for sciences). More precisely, in part 1 (dynamics for algorithm): I will talk about how to add momentum to gradient descent on a class of manifolds known as Lie groups. The treatment will be based on geometric mechanics and an interplay between continuous and discrete time dynamics. It will lead to accelerated optimization. Part 2 (dynamics for understanding deep learning) will be devoted to better understanding the nontrivial effects of large learning rates. I will describe how large learning rates could deterministically lead to chaotic escapes from local minima, which is an alternative mechanism to commonly known noisy escapes due to stochastic gradients. I will also mention another example, on an implicit regularization effect of large learning rates which is to favor flatter minimizers.  Part 3 (AI for sciences) will be on data-driven prediction of mechanical dynamics, for which I will demonstrate one strong benefit of having physics hard-wired into deep learning models; more precisely, how to make symplectic predictions, and how that generically improves the accuracy of long-time predictions.

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