Mathematics

We prove ?ojasiewicz inequalities for round cylinders and cylinders over Abresch-Langer curves, using perturbative analysis of a quantity introduced by Colding-Minicozzi. A feature is that this auxiliary quantity allows us to work essentially at first order. This new method interpolates between the higher order perturbative analysis used by the author for certain shrinking cylinders, and the differential geometric method used by Colding-Minicozzi for the round case.

Stratified groups are those simply connected Lie groups whose Lie algebras admit a derivation for which the eigenspace with eigenvalue 1 is Lie generating. When a stratified group is equipped with a left-invariant path distance that is homogeneous with respect to the automorphisms induced by the derivation, this metric space is known as Carnot group. Carnot groups appear in several mathematical contexts. To understand their algebraic structure, it is useful to study some examples explicitly. In this work, we provide a list of low-dimensional stratified groups, express their Lie product, and present a basis of left-invariant vector fields, together with their respective left-invariant 1-forms, a basis of right-invariant vector fields, and some other properties. We exhibit all stratified groups in dimension up to 7 and also study some free-nilpotent groups in dimension up to 14.

We use a theorem of P. Berger and D. Turaev to construct an example of a Finsler geodesic flow on the 2-torus with a transverse section, such that its Poincaré return map has positive metric entropy. The Finsler metric generating the flow can be chosen to be arbitrarily C ??-close to a flat metric.

We construct a concrete example of constant Gauss curvature K=1 on the 2-sphere having all geodesics closed and of same length.

By the classical Li-Yau inequality, an immersion of a closed surface in R n with Willmore energy below 8? has to be embedded. We discuss analogous results for curves in R 2 , involving Euler's elastic energy and other possible curvature functionals. Additionally, we provide applications to associated gradient flows.

We prove that if M is a strictly stable complete minimal hypersurface in Euclidean space with finite density at infinity and which lies on one side of a minimal cylinder with cross-section a strictly stable area minimizing hypercone, then M must be cylindrical. Applications will be given in the references [Sim20a], [Sim20b].

This work aims to interpolate parametrized Reduced Order Model (ROM) basis constructed via the Proper Orthogonal Decomposition (POD) to derive a robust ROM of the system's dynamics for an unseen target parameter value. A novel non-intrusive Space-Time (ST) POD basis interpolation scheme is proposed, for which we define ROM spatial and temporal basis \emph{curves on compact Stiefel manifolds}. An interpolation is finally defined on a \emph{mixed part} encoded in a square matrix directly deduced using the space part, the singular values and the temporal part, to obtain an interpolated snapshot matrix, keeping track of accurate space and temporal eigenvectors. Moreover, in order to establish a well-defined curve on the compact Stiefel manifold, we introduce a new procedure, the so-called oriented SVD. Such an oriented SVD produces unique right and left eigenvectors for generic matrices, for which all singular values are distinct. It is important to notice that the ST POD basis interpolation does not require the construction and the subsequent solution of a reduced-order FEM model as classically is done. Hence it is avoiding the bottleneck of standard POD interpolation which is associated with the evaluation of the nonlinear terms of the Galerkin projection on the governing equations. As a proof of concept, the proposed method is demonstrated with the adaptation of rigid-thermoviscoplastic finite element ROMs applied to a typical nonlinear open forging metal forming process. Strong correlations of the ST POD models with respect to their associated high-fidelity FEM counterpart simulations are reported, highlighting its potential use for near real-time parametric simulations using off-line computed ROM POD databases.

Let M be a smooth closed orientable manifold and P(M) the space of Poisson structures on M . We construct a Poisson bracket on P(M) depending on a choice of volume form. The Hamiltonian flow of the bracket acts on P(M) by volume-preserving diffeomorphism of M . We then define an invariant of a Poisson structure that describes fixed points of the flow equation and compute it for regular Poisson 3-manifolds, where it detects unimodularity. For unimodular Poisson structures we define a further, related Poisson bracket and show that for symplectic structures the associated invariant counting fixed points of the flow equation is given in terms of the d d Λ and d+ d Λ symplectic cohomology groups defined by Tseng and Yau.

Sharp bounds are given for solutions to the minimal surface equation with vanishing boundary values over domains containing sectors of opening bigger than pi.

We develop a sketching algorithm to find the point on the convex hull of a dataset, closest to a query point outside it. Studying the convex hull of datasets can provide useful information about their geometric structure and their distribution. Many machine learning datasets have large number of samples with large number of features, but exact algorithms in computational geometry are usually not designed for such setting. Alternatively, the problem can be formulated as a linear least-squares problem with linear constraints. However, solving the problem using standard optimization algorithms can be very expensive for large datasets. Our algorithm uses a sketching procedure to exploit the structure of the data and unburden the optimization process from irrelevant points. This involves breaking the data into pieces and gradually putting the pieces back together, while improving the optimal solution using a gradient project method that can rapidly change its active set of constraints. Our method eventually leads to the optimal solution of our convex problem faster than off-the-shelf algorithms.