| Analysis Determining wavenumber: the mathematical analogue of Kolmogorov’s dissipation number and Kraichnan’s number Mimi Dai Department of Mathematics, University of Illinois at Chicago |
This Analysis seminar may be attended Monday the 24th at 4:00 PM CDT (UT-5) via this Zoom link.
Meeting ID: 972 0263 8160
Passcode: 549916
| Biomathematics A case study: bio-control of aphids by generalist insect predators under field temperatures and in the warming world Nusrat Tabassum Department of Mathematics & Statistics, Texas Tech University |
| Algebra and Number Theory No Seminar |
| Geometry, PDE and Mathematical Physics Renormalized Area for Minimal Hypersurfaces of 5-Dimensional Poincare-Einstein Spaces (Part 2) Aaron Tyrrell Department of Mathematics and Statistics, Texas Tech University |
In 1999 Graham and Witten showed that one can define a notion of renormalized area for properly embedded minimal submanifolds of Poincare-Einstein spaces. For even-dimensional submanifolds, this quantity is a global invariant of the embedded submanifold. In 2008 Alexakis and Mazzeo wrote a paper on this quantity for surfaces in a 3-dimensional PE manifold, getting an explicit formula and studying its functional properties. We will look at a formula for the renormalized area of a minimal hypersurface of a 5-dimensional Poincare-Einstein space in terms of a Chern-Gauss-Bonnet formula.
Watch online via this Zoom link.
| Applied Mathematics and Machine Learning Eigenvalue Analysis for Rigorous Error Bounds in a Reduced Basis Method Jehanzeb H. Chaudhry Department of Mathematics and Statistics, University of New Mexico |
In the first part of this talk, we present a reduced basis method with a sharp error estimate with respect to the exact solution of the PDE. A crucial element in developing such a method is the least-squares finite element method (LSFEM). LSFEMs are widely used for the solution of PDEs arising in many applications in science and engineering. LSFEMs are based on minimizing the residual of the PDE in an appropriate norm, and have a number of attractive properties. In particular, the property relevant to this work is that these methods provide a robust and inexpensive a posteriori error estimate with respect to the true solution. This estimate is utilized in developing the Least-Squares Reduced Basis Method presented in this talk.
The second part of the talk concerns a key ingredient in error estimates for variational problems and reduced basis methods: the so-called coercivity or inf-sup constant of the continuous problem. We characterize the coercivity constant as a spectral value of a self-adjoint linear operator; for several differential equations, we show that the coercivity constant is related to the eigenvalue of a compact operator. For these applications, convergence rates are derived and verified with numerical examples.
Please attend this week's Applied Math seminar at 4 PM Wednesday via this Zoom link.
Meeting ID: 976 3095 1027
Passcode: applied
| Quantum Homotopy Model structures on simplicial presheaves. Dmitri Pavlov Department of Mathematics and Statistics, Texas Tech University |
| Mathematical Finance ESG investments: Filtering versus machine learning approaches Dr. Carmine de Franco Head of Research & ESG, Ossiam |
Bio: Carmine de Franco is the head of research at Ossiam, an asset management firm specializing in systematic and quantitative ETFs, located in Paris. Graduated in Mathematics from the University of Roma II - Tor Vergata and the University Paris VII - Denis Diderot, he holds a PhD in Probability and a master’s degree in Financial Random Modelling from the University Paris VII-Denis. Carmine joined Ossiam in May 2012 after working for 4 years at the Faculty of Mathematics of the University of Paris VII (Université Denis Diderot). His domain of expertise spans from mathematics and probability theory to statistics, from financial research to the design of investment strategy and cross-assets portfolio construction. More recently, his research topics have focused on ESG themes, low carbon approaches and biodiversity in financial investments, machine learning and artificial intelligence. He is co-author of several research papers on portfolio insurance, modelling and hedging with stochastic jumps, regime switching models, interest rates, equity, smart beta and factor investing, ESG, machine learning, Bayesian learning and portfolio construction under uncertainty, carbon and biodiversity.
Ossiam: is a Paris-based asset manager focused on quantitative and systematic investment solutions since 2009 with a distinct vision: providing clear, transparent access to quantitative, research-based strategies. Ossiam is an affiliate of Natixis Investment Managers and manages a range of ETFs, open ended-funds, dedicated funds and mandates across a variety of asset classes and themes. Ossiam is a signatory of the UN-supported Principles for Responsible Investment since 2016 and a signatory of the Finance for Biodiversity Pledge since 2021. As of end of July 2021, Ossiam had 5 bn EUR in assets under management.