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Departmental Colloquia

Our department is proud to host weekly colloquium talks featuring research by leading mathematicians from around the world. Most colloquia are held on Fridays at 4pm in Parker Hall, Room 250 (unless otherwise advertised) with refreshments preceding at 3:30pm in Parker Hall, Room 244. 

DMS Colloquium: Dr. Zuofeng Shang

Sep 20, 2019 04:00 PM

Speaker: Dr. Zuofeng Shang, Department of Mathematics and Statistics at Indiana University – Purdue University Indianapolis


Title: TBA

Abstract: TBA


Faculty host: Guanqun Cao

DMS Colloquium: Virginia Vassilevska Williams

Sep 27, 2019 04:00 PM


Speaker: Virginia Vassilevska Williams (MIT)

Title: TBA

DMS Colloquium: Beatrice Riviere

Sep 06, 2019 04:00 PM

Speaker: Béatrice Rivière, Rice University


Title: High order discontinuous Galerkin methods for solving the miscible displacement problem in porous media

Abstract: The accurate prediction of flow and transport in porous media is essential in optimizing the clean-up of contaminated groundwater or the production of hydrocarbons from oil reservoirs. In the miscible displacement problem, a solvent is injected and mixes with the resident fluid (contaminant or oil). The fluid mixture then propagates through the set of connected pores. At the Darcy scale, the mathematical model is a system of partial differential equations coupling flow and transport. This talk presents high order interior penalty discontinuous Galerkin (IPDG) methods and hybridizable discontinuous Galerkin (HDG) methods for solving the nonlinear system of equations. HDG methods retain the positive features of IPDG, but the number of globally coupled degrees of freedom for high order HDG is significantly smaller. The proposed numerical methods are shown to be accurate on coarse meshes when the polynomial degree increases. The numerical approximations of the solvent concentration exhibit sharp fronts even in highly heterogeneous media. Finally, the discontinuous Galerkin methods in space can accurately model viscous fingering. Viscous fingering in porous media may occur when a fluid with low viscosity is used to displace a fluid with high viscosity. For this type of flow instability, a tiny perturbation can be amplified exponentially, which triggers a finger-like pattern in the fluid concentration profile during the fluid displacement.  Simulations in two and in three dimensions show the growth and propagation of fingers for large mobility ratios and large Peclet numbers. Results are compared with those obtained by using a generic cell-centered finite volume method.


Short Bio-sketch
Béatrice Rivière is a Noah Harding Chair and Professor in the Department of Computational and Applied Mathematics at Rice University. She received her Ph.D. in 2000 from the University of Texas at Austin. Her other degrees include a Master in Mathematics in 1996 from Pennsylvania State University and an Engineering Diploma in 1995 from Ecole Centrale, France. She is the author of more than one hundred scientific publications in numerical analysis and scientific computation. Her book on the theory and implementation of discontinuous Galerkin methods is highly cited. Her research group is funded by the National Science Foundation, the oil and gas industry and the Gulf Coast Consortia for the Quantitative Biomedical Sciences.

Dr. Rivière has worked extensively of the development and analysis of numerical methods applied to problems in porous media and in fluid mechanics. Her current research deals with the development of high-order methods in time and in space for multiphase multicomponent flows (in rigid and deformable media); the modeling of pore scale flows for immiscible and miscible components; the numerical model of oxygen transport in a network of blood vessels; the analysis of PDE-based neural networks for image segmentation and the design of iterative solvers.

Dr. Rivière is an associate editor for the SIAM Journal on Numerical Analysis, for the SIAM Journal on Scientific Computing, for Results in Applied Mathematics, and a member of the editorial board for Advances in Water Resources. She has graduated a total of fourteen Ph.D. students, with eight working in academia and five in industry.


Faculty host: Thi-Thao-Phuong Hoang

DMS Colloquium: Wei Cai

Aug 30, 2019 04:00 PM

Speaker: Wei Cai, Southern Methodist University


Title: Algorithms for wave scattering of random media: Fast multipole method in layered media and a phase shift deep neural network for wideband learning


Abstract: In this talk, we will present two algorithms and numerical results for solving electromagnetic wave scattering of random meta-materials.  Firstly, a fast multipole method  for 3-D Helmholtz equation for layered media will be presented based on new multipole expansion (ME) and multipole to local translation (M2L) operators for layered media Green's functions. Secondly, a  parallel phase shift deep neural network (PhaseDNN) is proposed for wideband data learning. In order to achieve uniform convergence for low to high frequency content of data, phase shifts are used to convert high frequency learning to low frequency learning. Due to the fast learning of many DNNs in the low frequency range, PhaseDNN is able to learn wideband data uniformly in all frequencies.

Short Bio-sketch
Prof. Wei Cai obtained his B.S. and M.S. in Mathematics from the University of Science and Technology of China in 1982 and 1985, respectively, and his Ph.D. in Applied Mathematics at Brown University in 1989. Currently, he is the Clements Chair professor of applied mathematics at SMU. Before he joined SMU, he was an assistant and then associate professor at the University of California at Santa Barbara during 1995-96, and a full Professor at the University of North Carolina after 1999. He also taught and conducted research at Peking University, Fudan University, Shanghai Jiaotong University. His research interest focuses on the development of deterministic and stochastic numerical methods for studying electromagnetic and quantum phenomena with applications in meta-materials, nano-photonics, nano-electronics, biological systems, and quantum systems.  He has published over 110 refereed articles in top international journals  and is the author of the book "Computational Methods for Electromagnetic Phenomena: Electrostatics in Solvation, Scattering, and Electron Transport" published by Cambridge University Press, 2013. 


Faculty hosts: Junshan Lin and Yanzhao Cao

DMS Colloquium: Matthias Heikenschloss

Apr 26, 2019 04:00 PM


Speaker: Matthias Heikenschloss, Rice University

Title: Risk averse PDE constrained optimization


Abstract: Optimal control and optimal design problems governed by partial differential equations (PDEs) arise in many engineering and science applications. In these applications one wants to maximize the performance of the system subject to constraints. When problem data, such as material parameters, are not known exactly but are modeled as random fields, the system performance is a random variable. So-called risk measures are applied to this random variable to obtain the objective function for PDE constrained optimization under uncertainty. Instead of only maximizing expected performance, risk averse optimization also considers the deviation of actual performance below expected performance. The resulting optimization problems are difficult to solve, because a single objective function evaluation requires sampling of the governing PDE at many parameters. In addition, risk averse optimization requires sampling in the tail of the distribution. 

This talk demonstrates the impact of risk averse optimization formulations on the solution and illustrates the difficulties that arise in solving risk averse optimization. New sampling schemes are introduced that exploit the structure of risk measures and use reduced order models to identify the small regions in parameter space which are important for the optimization. It is shown that these new sampling schemes substantially reduce the cost of solving the optimization problems. 


Brief Bio and Research Summary 
Matthias Heinkenschloss joined the Rice faculty in 1996 after serving for three years as an assistant professor in the Department of Mathematics at Virginia Polytechnic Institute and State University. He rose through the ranks at Rice, and is now the Noah G. Harding Chair and Professor of Computational and Applied Mathematics. He served as department chair for six years. Matthias began his academic career at the University of Trier in the Federal Republic of Germany, where he was from 1988 to 1993.

Matthias Heinkenschloss’ research interests are in the design and analysis of mathematical optimization algorithms for nonlinear, large-scale (often infinite dimensional) problems and their applications to science and engineering problems. Research areas include large-scale nonlinear optimization, model order reduction, optimal control of partial differential equations (PDEs), optimization under uncertainty, PDE constrained optimization, iterative solution of KKT systems and domain decomposition in optimization.
Hosts: Yanzhao Cao and Hans-Werner van Wyk

DMS Colloquium: Frédéric Holweck

Apr 19, 2019 04:00 PM


Speaker: Frédéric Holweck, Université de Technologie de Belfort-Montbéliard (France)

Title: Projective duality and quantum information 

Abstract: Quantum Information is a nascent science which intends to use the properties of quantum physics to produce new computational paradigms. Quantum phenomena, like entanglement, are non-classical resources that need to be classified. Interestingly in the early 2000’s the old idea of projective duality regained new interest in the quantum physics literature as a potential tool for studying entanglement.

In this talk, after explaining the motivations from quantum physics, I will recall some classical notions regarding projective duality and introduce more recent results obtained with Luke Oeding about the calculation of hyperdeterminants from the \(E_8\)-discriminant.


Faculty host: Luke Oeding

DMS Colloquium: Youssef Marzouk

Apr 12, 2019 04:00 PM


Speaker: Youssef Marzouk,  MIT​

Title: Nonlinear filtering and smoothing with transport maps

Abstract: Bayesian inference for non-Gaussian state-space models is a ubiquitous problem, arising in applications from geophysical data assimilation to mathematical finance. We will present a broad introduction to these problems and then focus on high dimensional models with challenging nonlinear dynamics and sparse observations in space and time. While the ensemble Kalman filter (EnKF) yields robust ensemble approximations of the filtering distribution in this setting, it is limited by linear forecast-to-analysis transformations. To generalize the EnKF, we propose a methodology that transforms the non-Gaussian forecast ensemble at each assimilation step into samples from the current filtering distribution via a sequence of local nonlinear couplings. These couplings are based on transport maps that can be computed quickly using convex optimization, and that can be enriched in complexity to reduce the intrinsic bias of the EnKF. We discuss the low-dimensional structure inherited by the transport maps from the filtering problem, including decay of correlations, conditional independence, and local likelihoods. We then exploit this structure to regularize the estimation of the maps in high dimensions and with a limited ensemble size. 

We also present variational methods---again based on transport maps---for smoothing and sequential parameter estimation in non-Gaussian state-space models. These methods rely on results linking the Markov properties of a target measure to the existence of low-dimensional couplings, induced by transport maps that are decomposable. The resulting algorithms can be understood as a generalization, to the non-Gaussian case, of the square-root Rauch--Tung--Striebel Gaussian smoother.

This is joint work with Ricardo Baptista, Daniele Bigoni, and Alessio Spantini. 


Faculty hosts: Yanzhao Cao and Xiaoying Han

DMS Colloquium: Emanuele Ventura

Apr 05, 2019 04:00 PM


Speaker: Emanuele Ventura, Postdoc Texas A&M; Ph.D., Aalto University (Helsinki, Finland) 2017

Title: Tensors and their symmetry groups


Abstract: Tensors (multi-dimensional matrices) appear in many areas of pure and applied mathematics. I will discuss their use in algebraic complexity theory. Matrix multiplication is a tensor and its complexity is encoded in its tensor rank. To analyze the complexity of the matrix multiplication tensor, Strassen introduced a class of tensors that vastly generalize it, the tight tensors. These tensors have continuous symmetries. Pushing Strassen’s ideas forward, with A. Conner, F. Gesmundo, and J.M. Landsberg, we investigate tensors with large symmetry groups and classify them under a natural genericity assumption. Our study provides new paths towards upper bounds on the complexity of matrix multiplication.

Last Updated: 09/11/2015