## Ioannis Kevrekidis : No Equations, No Variables, No Parameters, No Space, No Time -- Data, and the Crystal Ball Modeling of Complex/Multiscale Systems

- Applied Math and Analysis ( 183 Views )Obtaining predictive dynamical equations from data lies at the heart of science and engineering modeling, and is the linchpin of our technology. In mathematical modeling one typically progresses from observations of the world (and some serious thinking!) first to selection of variables, then to equations for a model, and finally to the analysis of the model to make predictions. Good mathematical models give good predictions (and inaccurate ones do not) --- but the computational tools for analyzing them are the same: algorithms that are typically operating on closed form equations.

While the skeleton of the process remains the same, today we witness the development of mathematical techniques that operate directly on observations --- data, and appear to circumvent the serious thinking that goes into selecting variables and parameters and deriving accurate equations. The process then may appear to the user a little like making predictions by "looking into a crystal ball". Yet the "serious thinking" is still there and uses the same --- and some new --- mathematics: it goes into building algorithms that "jump directly" from data to the analysis of the model (which is now not available in closed form) so as to make predictions. Our work here presents a couple of efforts that illustrate this "new" path from data to predictions. It really is the same old path, but it is traveled by new means.

## Dan Hu : Optimization, Adaptation, and Initiation of Biological Transport Networks

- Applied Math and Analysis ( 180 Views )Blood vessel systems and leaf venations are typical biological transport networks. The energy consumption for such a system to perform its biological functions is determined by the network structure. In the first part of this talk, I will discuss the optimized structure of vessel networks, and show how the blood vessel system adapts itself to an optimized structure. Mathematical models are used to predict pruning vessels in the experiments of zebra fish. In the second part, I will discuss our recent modeling work on the initiation process of transport networks. Simulation results are used to illustrate how a tree-like structure is obtained from a continuum adaptation equation system, and how loops can exist in our model. Possible further application of this model will also be discussed.

## Mark Levi : Arnold diffusion in physical examples

- Applied Math and Analysis ( 168 Views )Arnold diffusion is the phenomenon of loss of stability of a completely integrable Hamiltonian system: an arbitrarily small perturbation can cause action to change along some orbit by a finite amount. Arnold produced the first example of diffusion and gave an outline of the proof. After a brief overview of related results I will describe the simplest example of Arnold diffusion which we found recently with Vadim Kaloshin. We consider geodesics on the 3-torus, or equivalently rays in a periodic optical medium in $ {\mathbb R} ^3 $ (or equivalently a point mass in a periodic potential in $ {\mathbb R} ^3 $.) Arnold diffusion has a transparent intuitive explanation and a simple proof. Resonances and the so-called ``whiskered tori" acquire a clear geometrical interpretation as well. I will conclude with a sketch of a different but related manifestation of Arnold diffusion as acceleration of a particle by a pulsating potential. This is joint work with Vadim Kaloshin.

## Paul Tupper : The Relation Between Shadowing and Approximation in Distribution

- Applied Math and Analysis ( 160 Views )In computational physics, molecular dynamics refers to the computer simulation of a material at the atomic level. I will consider classical deterministic molecular dynamics in which large Hamiltonian systems of ordinary differential equations are used, though many of the same issues arise with other models. Given its scientific importance there is very little rigorous justification of molecular dynamics. From the viewpoint of numerical analysis it is surprising that it works at all. The problem is that individual trajectories computed by molecular dynamics are accurate for only small time intervals, whereas researchers trust the results over very long time intervals. It has been conjectured that molecular dynamics trajectories are accurate over long time intervals in some weak statistical sense. Another conjecture is that numerical trajectories satisfy the shadowing property: that they are close over long time intervals to exact trajectories with different initial conditions. I will explain how these two views are actually equivalent to each other, after we suitably modify the concept of shadowing.

## Raanan Schul : Traveling Salesman type Results in quantitative rectifiability

- Applied Math and Analysis ( 160 Views )We will discuss several results concerning quantitative rectifiability in metric spaces, which generalize Euclidean results. We will spend some time explaining both the metric space results as well as their Euclidean counterparts. An example of such a result is a structure theorem, which characterizes subsets of rectifiable curves (the Analyst's Traveling Salesman theorem). This theory is presented in terms of multi-scale analysis and multi-scale constructions, and uses a language which is analogous to that of wavelets. Some of the results we will present will be dimension free.

## George Hagedorn : Some Theory and Numerics for Semiclassical Quantum Mechanics

- Applied Math and Analysis ( 160 Views )We begin with an introduction to time-dependent quantum mechanics and the role of Planck's constant. We then describe some mathematical results about solutions to the Schr\"odinger equation for small values of the Planck constant. Finally, we discuss two new numerical techniques for semiclassical quantum dynamics, including one that is a work in progress.

## Christel Hohenegger : Small scale stochastic dynamics: Application for near-weall velocimetry measurements

- Applied Math and Analysis ( 158 Views )Fluid velocities and Brownian effects at nanoscales in the near-wall r egion of microchannels can be experimentally measured in an image plane parallel to the wall, using for example, an evanescent wave illumination technique combi ned with particle image velocimetry [R. Sadr et al., J. Fluid Mech. 506, 357-367 (2004)]. Tracers particles are not only carried by the flow, but they undergo r andom fluctuations, the details of which are affected by the proximity of the wa ll. We study such a system under a particle based stochastic approach (Langevin) . We present the modeling assumptions and pay attention to the details of the si mulation of a coupled system of stochastic differential equations through a Mils tein scheme of strong order of convergence 1. Then we demonstrate that a maximum likelihood algorithm can reconstruct the out-of-plane velocity profile, as spec ified velocities at multiple points, given known mobility dependence and perfect mean measurements. We compare this new method with existing cross-correlation t echniques and illustrate its application for noisy data. Physical parameters are chosen to be as close as possible to the experimental parameters.

## Michael Gratton : Transient and self-similar dynamics in thin film coarsening

- Applied Math and Analysis ( 157 Views )Coarsening is the phenomenon where many objects (water drops, molecular islands, particles in a freezing liquid) becoming smaller in number but larger in size in an orderly way. This talk will examine modeling one such system, nanoscopic liquid drops, through three models: a PDE for the fluid, a coarsening dynamical system for the drops, and an LSW-type ensemble model for the distribution of drops. We will find self-similar solutions for the drop population valid for intermediate times and discuss transient effects that can delay the self-similar scaling.

## Karin Leiderman : A Spatial-Temporal Model of Platelet Deposition and Blood Coagulation Under Flow

- Applied Math and Analysis ( 157 Views )In the event of a vascular injury, a blood clot will form to prevent bleeding. This response involves two intertwined processes: platelet aggregation and coagulation. Activated platelets are critical to coagulation in that they provide localized reactive surfaces on which many of the coagulation reactions occur. The final product from the coagulation cascade directly couples the coagulation system to platelet aggregation by acting as a strong activator of platelets and cleaving blood-borne fibrinogen into fibrin which then forms a mesh to help stabilize platelet aggregates. Together, the fibrin mesh and the platelet aggregates comprise a blood clot, which in some cases, can grow to occlusive diameters. Transport of coagulation proteins to and from the vicinity of the injury is controlled largely by the dynamics of the blood flow. It is crucial to learn how blood flow affects the growth of clots, and how the growing masses, in turn, feed back and affect the fluid motion. We have developed the first spatial-temporal model of platelet deposition and blood coagulation under flow that includes detailed descriptions of the coagulation biochemistry, chemical activation and deposition of blood platelets, as well as the two-way interaction between the fluid dynamics and the growing platelet mass.

## Elizabeth L. Bouzarth : Modeling Biologically Inspired Fluid Flow Using RegularizedSingularities and Spectral Deferred Correction Methods

- Applied Math and Analysis ( 156 Views )The motion of primary nodal cilia present in embryonic development resembles that of a precessing rod. Implementing regularized singularities to model this fluid flow numerically simulates a situation for which colleagues have exact mathematical solutions and experimentalists have corresponding laboratory studies on both the micro- and macro-scales. Stokeslets are fundamental solutions to the Stokes equations, which act as external point forces when placed in a fluid. By strategically distributing regularized Stokeslets in a fluid domain to mimic an immersed boundary (e.g., cilium), one can compute the velocity and trajectory of the fluid at any point of interest. The simulation can be adapted to a variety of situations including passive tracers, rigid bodies and numerous rod structures in a fluid flow generated by a rod, either rotating around its center or its tip, near a plane. The exact solution allows for careful error analysis and the experimental studies provide new applications for the numerical model. Spectral deferred correction methods are used to alleviate time stepping restrictions in trajectory calculations. Quantitative and qualitative comparisons to theory and experiment have shown that a numerical simulation of this nature can generate insight into fluid systems that are too complicated to fully understand via experiment or exact numerical solution independently.

## Paolo Aluffi : Chern class identities from string theory

- Applied Math and Analysis ( 156 Views )(joint with Mboyo Esole) String theory considerations lead to a non-trivial identity relating the Euler characteristics of an elliptically fibered Calabi-Yau fourfold and of certain related surfaces. After giving a very sketchy idea of the physics arguments leading to this identity, I will present a Chern class identity which confirms it, generalizing it to arbitrary dimension and to varieties that are not necessarily Calabi-Yaus. The relevant loci are singular, and this plays a key role in the identity.

## Yuri Bakhtin : Noisy heteroclinic networks: small noise asymptotics

- Applied Math and Analysis ( 156 Views )I will start with the deterministic dynamics generated by a vector field that has several unstable critical points connected by heteroclinic orbits. A perturbation of this system by white noise will be considered. I will study the limit of the resulting stochastic system in distribution (under appropriate time rescaling) as the noise intensity vanishes. It is possible to describe the limiting process in detail, and, in particular, interesting non-Markov effects arise. There are situations where this result provides more precise exit asymptotics than the classical Wentzell-Freidlin theory.

## Seung-Yeal Ha : Uniform L^p-stability problem for the Boltzmann equation

- Applied Math and Analysis ( 155 Views )The Boltzmann equation governs the dynamics of a dilute gas. In this talk, I will address the L^p-stability problem of the Boltzmann equation near vacuum and a global Maxwellian. In a close-to-vacuum regime, I will explain the nonlinear functional approach motivated by Glimm's theory in hyperbolic conservation laws. This functional approach yields the uniform L^1-stability estimate. In contrast, in a close-to-global maxwellian regime, I will present the L^2-stability theory which establishes the uniform L^2-stability of several classical solutions.

## Hien Tran : HIV Model Analysis under Optimal Control Based Treatment Strategies

- Applied Math and Analysis ( 155 Views )In this talk, we will introduce a dynamic mathematical model that describes the interaction of the immune system with the human immunodeficiency virus (HIV). Using optimal control theory, we will illustrate that optimal dynamic multidrug therapies can produce a drug dosing strategy that exhibits structured treatment interruption, a regimen in which patients are cycled on and off therapy. In addition, sensitivity analysis of the model including both classical sensitivity functions and generalized sensitivity functions will be presented. Finally, we will describe how stochastic estimation can be used to filter and estimate states and parameters from noisy data. In the course of this analysis it will be shown that automatic differentiation can be a powerful tool for this type of study.

## Laurent Demanet : Time upscaling of wave equations via discrete symbol calculus

- Applied Math and Analysis ( 154 Views )The complexity of solving the time-dependent wave equation via traditional methods scales faster than linearly in the complexity of the initial data. This behavior is mostly due to the necessity of timestepping at the CFL level, and is hampering the resolution of large-scale inverse scattering problems such as reflection seismology, where massive datasets need to be processed. In this talk I will report on some algorithmic progress toward time upscaling of the wave equation using discrete symbol calculus for pseudodifferential and Fourier integral operators. Joint work with Lexing Ying from UT Austin.

## Peter Diao : Model-Free Consistency of Graph Partitioning using Dense Graph Limits

- Applied Math and Analysis ( 154 Views )The beautiful work of Borgs, Chayes, Lovasz, Sos, Szegedy, Vesztergombi, and many others on dense graph limits has received quite a bit of attention in pure math as well as statistics and machine learning. In this talk we will review some of the previous work on dense graph limits and then present recent work on providing a more robust mathematical framework for proving the statistical consistency of graph partitioning algorithms such as spectral clustering. A striking feature of our approach is that it is model-free, compared to the popular iid paradigm. Our results are thus broadly applicable in real-world settings, where it is notoriously difficult to obtain relevant models for network data, and observations are not independent. At the end, I will discuss implications for how mathematical foundations can be developed for other modern data analysis techniques. This is joint work with Dominique Guillot, Apoorva Khare, and Bala Rajaratnam. Preprint available at https://arxiv.org/abs/1608.03860.

## Scott McKinley : Fluctuating Hydrodynamics of Polymers in Dilute Solution

- Applied Math and Analysis ( 153 Views )In 1953, the physicist P.E. Rouse proposed to model polymers in dilute solution by taking the polymer to be a series of beads connected by Gaussian springs. Neglecting inertia, the dynamics are set by a balance between the thermal fluctuations in the fluid and the elastic restoring force of the springs. One year later, B. Zimm noted that a polymer will interact with itself through the fluid in a qualitatively meaningful way. In this talk, we consider a more recent Langevin equation approach to dealing with hydrodynamic self-interaction. This involves coupling the continuum scaling limit of the Rouse model with stochastically forced time-dependent Stokes equations. The resulting pair of parabolic SPDE, with non-linear coupled forcing, presents a number of mathematical challenges. On the way to providing an existence and uniqueness result, we shall take time to develop relevant stochastic tools, and consider the modeling implications of certain technical results.

## Dejan Slepcev : Variational problems on graphs and their continuum limit

- Applied Math and Analysis ( 152 Views )I will discuss variational problems arising in machine learning and their limits as the number of data points goes to infinity. Consider point clouds obtained as random samples of an underlying "ground-truth" measure. Graph representing the point cloud is obtained by assigning weights to edges based on the distance between the points. Many machine learning tasks, such as clustering and classification, can be posed as minimizing functionals on such graphs. We consider functionals involving graph cuts and graph laplacians and their limits as the number of data points goes to infinity. In particular we establish for what graph constructions the minimizers of discrete problems converge to a minimizer of a functional defined in the continuum setting. The talk is primarily based on joint work with Nicolas Garcia Trillos, as well as on works with Xavier Bresson, Moritz Gerlach, Matthias Hein, Thomas Laurent, James von Brecht and Matt Thorpe.

## Ralph Smith : Model Development and Control Design for High Performance Nonlinear Smart Material Systems

- Applied Math and Analysis ( 152 Views )High performance transducers utilizing piezoceramic, electrostrictive, magnetostrictive or shape memory elements offer novel control capabilities in applications ranging from flow control to precision placement for nanoconstruction. To achieve the full potential of these materials, however, models, numerical methods and control designs which accommodate the constitutive nonlinearities and hysteresis inherent to the compounds must be employed. Furthermore, it is advantageous to consider material characterization, model development, numerical approximation, and control design in concert to fully exploit the novel sensor and actuator capabilities of these materials in coupled systems.

In this presentation, the speaker will discuss recent advances in the development of model-based control strategies for high performance smart material systems. The presentation will focus on the development of unified nonlinear hysteresis models, inverse compensators, reduced-order approximation techniques, and nonlinear control strategies for high precision or high drive regimes. The range for which linear models and control methods are applicable will also be outlined. Examples will be drawn from problems arising in structural acoustics, high speed milling, deformable mirror design, artificial muscle development, tendon design to minimize earthquake damage, and atomic force microscopy.

## Yu Chen : AM and FM Approaches to Sensing and Imaging

- Applied Math and Analysis ( 152 Views )In radio signal encoding and decoding, frequency modulation (FM) has several advantages over amplitude modulation (AM) - we all enjoy the high fidelity and nearly static free reception of FM radio. Sensing and imaging can also be approached with AM or FM modalities. All imaging methods practiced today are AM implementations, and FM for imaging has never been explored or its advantages exploited. In this talk I'll introduce the FM approach to sensing and imaging in its infant form. I'll show that the FM approach is closely related to design of Gaussian quadratures for bandlimited functions. I'll demonstrate the superiorities of the FM approach over AM by proposing three FM methods to deal with Gibbs phenomenon encountered in imaging.

For a more detailed abstract, see http://www.math.duke.edu/~jonm/yuChen.html

## Robert Pego : Scaling dynamics of solvable models of coagulation

- Applied Math and Analysis ( 150 Views )We study limiting behavior of rescaled size distributions in several models of clustering or coagulation dynamics, `solvable' in the sense that the Laplace transform converts them into nonlinear PDE. The scaling analysis that emerges has many connections with the classical limit theorems of probability theory, and a surprising application to the study of shock clustering in the inviscid Burgers equation with random-walk initial data. This is joint work with Govind Menon.

## Mary Lou Zeeman : Modeling the Menstrual Cycle:How does estradiol initiate the LH surge?

- Applied Math and Analysis ( 150 Views )In vertebrates, ovulation is triggered by a surge of luteinizing hormone (LH) from the pituitary. The precise mechanism by which rising estradiol (E2) from the ovaries initiates the LH surge in the human menstrual cycle remains a mystery. The mystery is due in part to the bimodal nature of estradiol feedback action on LH secretion, and in part to disagreement over the site of the feedback action.

We will describe a differential equations model in which the mysterious bimodality of estradiol action arises from the electrical connectivity of a network of folliculo-stellate cells in the pituitary. The mathematical model is based as closely as possible on current experimental data, and is being used to design and conduct new experiments. No biological background will be assumed.

## Jon Wilkening : Traveling-Standing Water Waves and Microseisms

- Applied Math and Analysis ( 148 Views )We study a two-parameter family of solutions of the surface Euler equations in which solutions return to a spatial translation of their initial condition at a later time. Pure standing waves and pure traveling waves emerge as special cases at fixed values of one of the parameters. We find many examples of wave crests that nearly sharpen to a corner, with corner angles close to 120 degrees near the traveling wave of greatest height, and close to 90 degrees for large-amplitude pure standing waves. However, aside from the traveling case, we do not believe any of these solutions approach a limiting extreme wave that forms a perfect corner. We also compute nonlinear wave packets, or breathers, which can take the form of NLS-type solitary waves or counterpropagating wave trains of nearly equal wavelength. In the latter case, an interesting phenomenon occurs in which the pressure develops a large DC component that varies in time but not space, or at least varies slowly in space compared to the wavelength of the surface waves. These large-scale pressure zones can move very rapidly since they travel at the envelope speed, and may be partially responsible for microseisms, the background noise observed in earthquake seismographs.

## Giang Tran : Sparsity-Inducing Methods for Nonlinear Differential Equations

- Applied Math and Analysis ( 147 Views )Sparsity plays a central role in recent developments of many fields such as signal and image processing, compressed sensing, statistics, and optimization. In practice, sparsity is promoted through the additional of an L1 norm (or related quantity) as a constraint or penalty in a variational model. Motivated by the success of sparsity-inducing methods in imaging and information sciences, there is a growing interest in exploiting sparsity in dynamical systems and partial differential equations. In this talk, we will investigate the connections between compressed sensing, sparse optimization, and numerical methods for nonlinear differential equations. In particular, we will discuss about sparse modeling as well as the advantage of sparse optimization in solving various differential equations arising from physical and data sciences.

## Joceline Lega : Molecular dynamics simulations of live particles

- Applied Math and Analysis ( 145 Views )I will show results of molecular dynamics simulations of hard disks with non-classical collision rules. In particular, I will focus on how local interactions at the microscopic level between these particles can lead to large-scale coherent dynamics at the mesoscopic level.

This work is inspired by collective behaviors, in the form of vortices and jets, recently observed in bacterial colonies. I will start with a brief summary of basic experimental facts and review a hydrodynamic model developed in collaboration with Thierry Passot (Observatoire de la Cote d'Azur, Nice, France). I will then motivate the need for a complementary approach that includes microscopic considerations, and describe the principal computational issues that arise in molecular dynamics simulations, as well as the standard ways to address them. Finally, I will discuss how classical collision rules that conserve energy and momentum may be modified to describe ensembles of live particles, and will show results of numerical simulations in which such rules have been implemented. Randomness, included in the form of random reorientation of the direction of motion of the particles, plays an important role in the type of collective behaviors that are observed.

## Janet Best : Parkinsons: two mathematical views of a neurological disease

- Applied Math and Analysis ( 145 Views )Parkinson's Disease (PD) is the most common movement disorder in the United States, with symptoms due to progressive loss of neurons within the basal ganglia. In the first part of the talk, we present and analyze a minimal model for the lack of cross-correlations in neuronal activity in the healthy basal ganglia.

The second part of the talk focuses on experimentally-observed changes in neuronal firing patterns that accompany PD and that may result in the motor symptoms. We have constructed a neuronal network model for the increases in correlated activity within the basal ganglia following the onset of PD. We then apply dynamical systems methods to understand transitions between irregular and rhythmic, correlated firing in the model. Geometric singular perturbation theory and one-dimensional maps are used to understand how an excitatory-inhibitory neuronal network with fixed architecture can generate both activity patterns for possibly different values of the intrinsic and synaptic parameters. We discuss hypotheses arising from the model as well as ongoing experiments to test these predictions.

## Per-Gunnar Martinsson : Fast numerical methods for solving linear PDEs

- Applied Math and Analysis ( 144 Views )Linear boundary value problems occur ubiquitously in many areas of science and engineering, and the cost of computing approximate solutions to such equations is often what determines which problems can, and which cannot, be modelled computationally. Due to advances in the last few decades (multigrid, FFT, fast multipole methods, etc), we today have at our disposal numerical methods for most linear boundary value problems that are "fast" in the sense that their computational cost grows almost linearly with problem size. Most existing "fast" schemes are based on iterative techniques in which a sequence of incrementally more accurate solutions is constructed. In contrast, we propose the use of recently developed methods that are capable of directly inverting large systems of linear equations in almost linear time. Such "fast direct methods" have several advantages over existing iterative methods: (1) Dramatic speed-ups in applications involving the repeated solution of similar problems (e.g. optimal design, molecular dynamics). (2) The ability to solve inherently ill-conditioned problems (such as scattering problems) without the use of custom designed preconditioners. (3) The ability to construct spectral decompositions of differential and integral operators. (4) Improved robustness and stability. In the talk, we will also describe how randomized sampling can be used to rapidly and accurately construct low rank approximations to matrices. The cost of constructing a rank k approximation to an m x n matrix A for which an O(m+n) matrix-vector multiplication scheme is available is O((m+n)*k). This cost is the same as that of Lanczos, but the randomized scheme is significantly more robust. For a general matrix A, the cost of the randomized scheme is O(m*n*log(k)), which should be compared to the O(m*n*k) cost of existing deterministic methods.

## Nathan Totz : A Rigorous Justification of the Modulation Approximation to the 2D Full Water Wave Problem

- Applied Math and Analysis ( 143 Views )In this joint work with Sijue Wu (U. Mich.), we consider solutions to the 2D
inviscid infinite depth water wave problem neglecting surface tension which
are to leading order wave packets of the form $\alpha + \epsilon B(\epsilon
\alpha, \epsilon t, \epsilon^2 t)e^{i(k\alpha + \omega t)}$ for $k > 0$.
Multiscale calculations formally suggest that such solutions have
modulations $B$ that evolve on slow time scales according to a focusing
cubic NLS equation. Justifying this rigorously is a real problem, since
standard existence results do not yield solutions which exist for long
enough to see the NLS dynamics. Nonetheless, given initial data within
$O(\epsilon^{3/2})$ of such wave packets in $L^2$ Sobolev space, we show
that there exists a unique solution to the water wave problem which remains
within $O(\epsilon^{3/2})$ to the approximate solution for times of order
$O(\epsilon^{-2})$. This is done by using a version of the evolution
equations for the water wave problem developed by Sijue Wu with no quadratic nonlinearity.

See arXiv:1101.0545

## Mark Iwen : Signal Recovery via Discrete Measurement Matrices

- Applied Math and Analysis ( 143 Views )We will discuss a class of binary measurement matrices having a simple discrete incoherence property. These matrices can be shown to have both useful analytic (i.e., restricted isometry and l1-approximation properties) and combinatorial (i.e., group testing and expander graph related) structure which allows them to be utilized for sparse signal approximation in the spirit of compressive sensing. In addition, their structure allows the actual signal recovery process to be carried out by highly efficient algorithms once measurements have been taken. One application of these matrices and their related recovery algorithms is their application to the development of sublinear-time Fourier methods capable of accurately approximating periodic functions using far fewer samples and run time than required by standard Fourier transform techniques.