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Today

Paris Perdikaris, University of of Pennsylvania, When and why physics-informed neural networks fail to train: A neural tangent kernel perspective

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Physics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial differential equations. However, despite their noticeable empirical success, little is known about how such constrained neural networks behave during their training via gradient descent. More importantly, even less is known…

Geometry/Topology Social Hour

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Come chat with other geometers/topologists.  This is a good chance for graduate students to meet the geometry/topology faculty, especially our newest members, Peter McGrath and Teemu Saksala.   Host: Tye Lidman (tlid@math.ncsu.edu) Instructions to join: Zoom invitation is sent to the geometry and topology seminar list. If you are not on the list, please, contact the…

Luis Briceno, Universidad Técnica Federico Santa María, Chile, Splitting algorithms for non-smooth convex optimization: Review, projections, and applications

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In this talk we review some classical algorithms for solving structured convex optimization problems, passing from gradient descent to proximal iterations and going further to modern proximal primal-dual splitting algorithms in the case of more complicated objective functions. We put special attention to constrained convex optimization, in which we accelerate the performance of the algorithms…

Teemu Saksala NC State, Probing an unknown elastic body with waves that scatter once. An inverse problem in anisotropic elasticity.

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We consider a geometric inverse problem of recovering some material parameters of an unknown elastic body by probing with elastic waves that scatter once inside the body. That is we send elastic waves from the boundary of an open bounded domain. The waves propagate inside the domain and scatter from an unknown point scatterer. We measure the entering…

Boris Muha, University of Zagreb, Croatia, Analysis of Moving Boundary Fluid-Structure Interaction Problems Arising in Hemodynamics

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Fluid-structure interaction (FSI) problems describe the dynamics of multi-physics systems that involve fluid and solid components. These are everyday phenomena in nature, and arise in various applications ranging from biomedicine to engineering. Mathematically, FSI problems are typically non-linear systems of partial differential equations (PDEs) of mixed hyperbolic-parabolic type, defined on time-changing domains. In this lecture…

Miruna-Stefana Sorea, Max-Planck-Institut für Mathematik in den Naturwissenschaften, The shapes of level curves of real polynomials near strict local minima

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We consider a real bivariate polynomial function vanishing at the origin and exhibiting a strict local minimum at this point. We work in a neighbourhood of the origin in which the non-zero level curves of this function are smooth Jordan curves. Whenever the origin is a Morse critical point, the sufficiently small levels become boundaries…

Zakhar Kabluchko, University of Münster, Germany, Expected f-vector of the Poisson Zero Cell

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The Poisson hyperplane process describes, roughly speaking, infinitely many hyperplanes thrown uniformly at random into the d-dimensional Euclidean space. The hyperplanes dissect the space into countably many cells. The a.s. unique cell containing the origin is called the Poisson zero polytope. We prove an explicit combinatorial formula for the expected number of k-dimensional faces of…

Rupert L. Frank, California Institute of Technology, A ‘liquid-solid’ phase transition in a simple model for swarming

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We consider a non-local optimization problem, which is motivated by a simple model for swarming and other self-assembly/aggregation models, and prove the existence of different phases. In particular, we show that in the large mass regime the ground state density profile is the characteristic function of a round ball. An essential ingredient in our proof…

Jordan Altmeter, NC State, Hypercube Graph Associahedra

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The associahedron is a well-studied polytope. For n dimensions, its vertices are counted by the n-th Catalan number, a sequence starting 1,1,2,5,14,42,... and which counts many, many, many combinatorial objects, such as Dyck paths, planar binary trees, noncrossing set partitions, and polygonal triangulation. There is a well-known generalization of the associahedron, called the graph associahedron,…

Thomas Courtade, UC Berkeley

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Website: https://sites.google.com/view/paw-seminar Host: Paata Ivanisvili  pivanis@ncsu.edu

Darrick Lee Affiliation, University of Pennsylvania, Path Signatures on Lie Groups

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Path signatures are powerful nonparametric tools for time series analysis, shown to form a universal and characteristic feature map for Euclidean valued time series data. The theory of path signatures can be lifted to the setting of Lie group valued time series while retaining their universal and characteristic properties. This talk will introduce these generalized path signatures on Lie groups and…

Petronela Radu, University of Nebraska-Lincoln, USA, Nonlocal models: theoretical and applied aspects

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The emergence of nonlocal theories as promising models in different areas of science (continuum mechanics, biology, image processing) has led the mathematical community to conduct varied investigations of systems of integro-differential equations. In this talk I will present some recent results on systems of integral equations with weakly singular kernels, flux-type boundary conditions, as well…