Organizer: Dmitry Dolgopyat and Leonid Koralov
When: Wednesdays @ 2pm
Where: Math 1313

Archives: 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017

  • Quasi-linear parabolic PDE's with singular inputs

    Speaker: Scott Andrew Smith (Max Plank Institute, Leipzig) -

    When: Wed, September 27, 2017 - 11:00am
    Where: Kirwan Hall 3206

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    Abstract: The present talk is concerned with quasi-linear parabolic equations which are ill-posed in the classical distributional sense. In the semi-linear context, the theory of regularity structures provides a solution theory which applies to a large class of equations with suitably randomized inputs. The quasi-linear setting has seen recent advances, but a general theory remains open. We will present some partial progress in this direction based on joint work with Felix Otto, Jonas Sauer, and Hendrik Weber.

  • Edgeworth expansions for weakly dependent random variables

    Speaker: Kasun Fernando (UMD, Mathematics) -

    When: Wed, October 11, 2017 - 11:00am
    Where: Kirwan Hall 3206

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    Abstract: We discuss sufficient conditions that guarantee the existence of asymptotic expansions for the CLT for weakly dependent random variables including observations arising from sufficiently chaotic dynamical systems like piece-wise expanding maps and strongly ergodic Markov chains. We primarily use spectral techniques to obtain the results. This is a joint work with Carlangelo Liverani.
  • Well-posedness for Stochastic Continuity Equations with Rough Coefficients

    Speaker: Sam Punshon-Smith (University of Maryland, Mathematics Department) -

    When: Wed, November 1, 2017 - 11:00am
    Where: Kirwan Hall 3206

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    Abstract: According to the theory of Diperna/ Lions, the continuity equation associated to a Sobolev (or BV) vector field with bounded divergence has a unique weak solution in L^\infty. Under the addition of a white in time stochastic perturbation to the characteristics of the continuity equation, it is known that existence and uniqueness can be obtained under a relaxation of the regularity conditions and the requirement of bounded divergence. In this talk, we consider the general stochastic continuity equation associated to an Itô diffusion with irregular drift and diffusion coefficients. This equation has applications in the modeling of turbulent flows and complex fluids. We will discuss two types of probabilistically strong, analytically weak solutions to this equation and give conditions under which the equation has a unique solution. Using these results, we outline a proof of uniqueness of solutions to the stochastic transport equation with drift in L^q_t L^p_x, satisfying the sub-critical Ladyzhenskaya–Prodi–Serrin criterion 2/q + d/p < 1. Connections to existence, uniqueness and regularity of the stochastic flow associated to the SDE will be discussed.
  • Challenges and Implications of Autocorrelation in Animal Movement Studies

    Speaker: Bill Fagan (Department of Biology, University of Maryland) -

    When: Wed, November 8, 2017 - 11:00am
    Where: Kirwan Hall 3206

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    Abstract: The interface between basic ecology and applied mathematics is robust, and results from this interface are often critical to effective conservation. In this Probability/Applied Math seminar, I will focus on one part of this interface whereby ecological observations and datasets have created new opportunities for a variety of mathematical tools and approaches. For instance, datasets derived from efforts to track the movements of wild animals (e.g., using GPS-satellite devices) have presented new applications for research on stochastic processes. As technology has improved and datasets have expanded, autocorrelations in both animal position and animal velocity have become key features that can no longer be ignored. Instead there is a need to embrace the information content of the autocorrelation structure of tracking datasets and use that information to obtain biological understanding. Examples include applications of semi-variograms, which identify multiple movement modes and solve the sampling rate problem for tracking data, and autocorrelated kernel density estimators, which provide valuable new approaches for delineating animal home ranges.