Oct 20, 2025
Monday
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09:15 AM - 09:30 AM
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Welcome to SLMath
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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09:30 AM - 10:30 AM
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Critical trajectories in kinetic geometry
Clément Mouhot (Center for Mathematical Sciences)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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We construct critical trajectories in kinetic geometry, i.e. curves in (t,x,v) that are tangential to the transport and v-gradient, connecting any two given points, respecting the underlying kinetic scaling, and matching scaling properties of the stochastic trajectories near the starting point. The construction is based on solving the laws of motions with a forcing made up of desynchronised logarithmic oscillations. These critical trajectories provide an ''almost exponential map'' that allows to prove several functional analytic estimates. In particular they allow to extend to the kinetic setting the universal estimate for the logarithm of positive supersolutions by Moser 1971, and deduce optimal (weak) Harnack inequalities.
- Supplements
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10:30 AM - 11:00 AM
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Break
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11:00 AM - 12:00 PM
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Diffusive behaviour of some linear kinetic equations
José Cañizo (University of Granada)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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We consider linear kinetic equations of the form $\partial_t f + \frac{1}{\epsilon} v \nabla_x f = \frac{1}{\epsilon^2} L(f)$, for an unknown $f$ which depends on time $t$, position $x$ and velocity $v$, and where $L$ is a linear operator which acts only in the velocity variable, and which typically has a probability equilibrium in $v$. Important examples include the Fokker-Planck operator, nonlocal diffusion operators, linear BGK-type operators, or linear Boltzmann operators. This PDE typically represents a mesoscopic physical model, where we keep track of the probability distribution of the position and velocity of particles. It is well known that when $\epsilon$ tends to $0$, this type of equation has a macroscopic or diffusive limit for the density $\rho(t,x) := \int f(t,x,v) dv$, which is either the standard heat equation, or the fractional heat equation. As a new result, we show that for a fixed epsilon, the behaviour of this equation for large times also follows the standard or fractional heat equation, and that the long-time and small-epsilon limits are actually interchangeable in many cases. This is a work in collaboration with Stéphane Mischler (U. Paris-Dauphine) and Niccolò Tassi (U. Granada).
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12:00 PM - 01:30 PM
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Lunch
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01:30 PM - 02:30 PM
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Forward and inverse problems of a semilinear transport equation
Kui Ren (Columbia University)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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We study forward and inverse problems for a semilinear radiative transport model where the absorption coefficient depends on the angular average of the transport solution. Our first result is the well-posedness theory for the transport model with general boundary data, which significantly improves previous theories for small boundary data. For the inverse problem of reconstructing the nonlinear absorption coefficient from internal data, we develop stability results for the reconstructions and unify an $L^1$ stability theory for both the diffusion and transport regimes by introducing a weighted norm that penalizes the contribution from the boundary region. The problems studied here are motivated by applications such as photoacoustic imaging of multi-photon absorption of heterogeneous media.This is based on a joint work with Yimin Zhong of Auburn University.
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02:30 PM - 03:30 PM
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Using the unified thermodynamic (UT) algorithm for constructing metriplectic systems, such as `exotic’ collision operator
Philip Morrison (University of Texas, Austin)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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03:30 PM - 04:00 PM
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Afternoon Tea
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- Location
- SLMath: Atrium
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04:00 PM - 05:00 PM
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Suppressing Plasma Instability Through Constrained Optimization
Li Wang (University of Minnesota)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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Maintaining the stability and shape of a plasma is a crucial task in fusion energy. This is often challenging as plasma systems tend to be naturally unstable and kinetic effects can play an important role in the behavior of the instabilities. In this talk, I will introduce PDE-constrained optimization formulation that uses a kinetic description of plasma dynamics, particularly the Vlasov–Poisson system as the governing constraint. I will then discuss strategies to reduce computational costs through moment control. To further address the challenge of real-time control of plasma instabilities over long time horizons, we develop a dynamic feedback control strategy. This involves constructing an operator that maps state perturbations to an external control field. This operator is either approximated by a simple neural network, or directly constructed via energy estimates and a cancellation-based control approach that neutralizes the destabilizing components of the electric field.
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Oct 21, 2025
Tuesday
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01:30 PM - 02:30 PM
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Explicit construction of zero loss minimizers and the interpretability problem in Deep Learning
Thomas Chen (University of Texas at Austin)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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In this talk, we present some recent results aimed at the rigorous mathematical understanding of how and why supervised learning works. We point out genericness conditions related to reachability of zero loss minimization, in underparametrized versus overparametrized Deep Learning (DL) networks. For underparametrized DL networks, we explicitly construct global, zero loss cost minimizers for sufficiently clustered data. In addition, we derive effective equations governing the cumulative biases and weights, and show that gradient descent corresponds to a dynamical process in the input layer, whereby clusters of data are progressively reduced in complexity ("truncated") at an exponential rate that increases with the number of data points that have already been truncated. For overparametrized DL networks, we prove that the gradient descent flow is homotopy equivalent to a geometrically adapted flow that induces a (constrained) Euclidean gradient flow in output space. If a certain rank condition holds, the latter is, upon reparametrization of the time variable, equivalent to simple linear interpolation. This in turn implies zero loss minimization and the phenomenon known as “Neural Collapse”. Moreover, we derive zero loss guarantees, and construct explicit global minimizers for overparametrized deep networks, given generic training data. This is applied to derive deterministic generalization bounds that depend on the geometry of the training and test data, but not on the network architecture. The work presented includes collaborations with Patricia Munoz Ewald, Andrew G. Moore, and C.-K. Kevin Chien.
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02:30 PM - 03:30 PM
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Flow Maps: Flow-based generative models with lightning-fast inference
Nicholas Boffi (Carnegie Mellon University)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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Flow-based models have spurred a revolution in generative modeling, driving astounding advancements across diverse domains including high-resolution text to image synthesis and de-novo drug design. Yet despite their remarkable performance, inference in these models requires the solution of a differential equation, which is extremely costly for the large-scale neural network-based models used in practice. In this talk, we introduce a mathematical theory of flow maps, a new class of generative models that directly learn the solution operator for a flow-based model. By learning this operator, flow maps can generate data in 1-4 network evaluations, leading to orders of magnitude faster inference compared to standard flow-based models. We discuss several algorithms for efficiently learning flow maps in practice that emerge from our theory, and we show how many popular recent methods for accelerated inference -- including consistency models, shortcut models, and mean flow -- can be viewed as particular cases of our formalism. We demonstrate the practical effectiveness of flow maps across several tasks including image synthesis, geometric data generation, and inference-time guidance of pre-trained text-to-image models.
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03:30 PM - 04:00 PM
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Afternoon Tea
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- Location
- SLMath: Atrium
- Video
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04:00 PM - 05:00 PM
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Generative artificial intelligence methods for particle-based kinetic computations
Diego Del-Castillo-Negrete (University of Texas, Austin)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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We report recent progress on the use of generative artificial intelligence (AI) methods to accelerate particle-based plasma kinetic computations. These computations are time-consuming due to multiscale dynamics, boundary conditions, and the need to follow large ensembles of particles to avoid statistical sampling errors. The physics models of interest are Fokker-Planck (FP) equations for the particle distribution function in phase space including drifts, diffusion, and collisions. The AI methods include Normalizing Flows (NF) and Diffusion Models (DM). We present a pseudo-reversible NF model that learns the distribution of the final state conditioned to the initial state, such that the model only needs to be trained once and then used to handle arbitrary initial conditions [1]. Following this, we present results based on the use of DM that allow the quantification of confinement losses in bounded domains. We propose a unified hybrid data-driven approach that combines a conditional DM with an exit prediction neural network to capture both interior stochastic dynamics and boundary exit phenomena [2]. Convergence analysis, along with numerical test experiments are provided to demonstrate the effectiveness of the proposed methods. We present applications to advection-diffusion transport in 3D chaotic flows, and the generation and confinement of runaway electrons in magnetically confined fusion plasmas.
[1] M. Yang et al, SIAM Journal of Scientific Computing, 46, (4) C508-C533 (2024).
[2] M. Yang et al, Submitted to J. Comp. Phys. arXiv:2507.15990v1 (2025).
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05:00 PM - 06:20 PM
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Reception
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- Location
- SLMath: Atrium
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Oct 22, 2025
Wednesday
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09:30 AM - 10:30 AM
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Global in time stability of equilibrium for general relativistic Boltzmann equation in the massless Robertson-Walker spacetime
Robert Strain (University of Pennsylvania)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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The general relativistic Boltzmann equation is a fundamental physical model in astrophysics, for example in systems of galaxies, in supernova explosions, as a model of the early universe, and additionally as a model for hot gasses and plasmas. The general relativistic Boltzmann equation with the Robertson-Walker metric in the massless case admits a family of non-stationary Equilibria of the form $J((t^q)^2 p) = \exp(-(t^q)^2 |p|)$. The Robertson-Walker metric, or Friedmann–Lemaître–Robertson–Walker metric, is a widely used model describing a homogeneous isotropic and expanding universe. In this work, for $0< q < 1$, we prove the global-in-time existence and uniqueness of suitably small perturbations of these Equilibria. For $0< q < 1/3$ we prove that the perturbation has the superpolynomial large time-decay rate of $\exp(-t^{1-3q})$, and for $1/3< q < 1$ the perturbation has a slower time-decay rate of $t^{-3q}$.
This is a joint work with Martin Taylor and Renato Velozo Ruiz (both of Imperial College in London).
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10:30 AM - 11:00 AM
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Break
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11:00 AM - 12:00 PM
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Toward overcoming computational barriers via phase-space hp-adaptivity, for atmospheric radiative transfer and general kinetic equations
Samuel Stechmann (University of Wisconsin-Madison)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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Computational expense is a bottleneck for many kinetic equations, due to the high-dimensionality of position-velocity phase space. As an approach to alleviating this issue, I will present results that use hp-adaptive mesh refinement in both position space and velocity space. As a specific application, I will show promising results for atmospheric radiative transfer, toward overcoming the 1D barrier and allowing 3D radiative transfer with clouds. More generally, I will describe our efforts to create a general-purpose software package for any kinetic equation. The aim of the software (called Jexpresso, work in progress) is to utilize continuous Galerkin or discontinuous Galerkin spectral element methods with adaptivity for efficiency, combined with a user-friendly interface to allow a user to easily specify and solve any kinetic equation.
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Oct 23, 2025
Thursday
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01:30 PM - 02:30 PM
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The Landau equation and Fisher information
Nestor Guillen (Texas State University)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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In recent work with Luis Silvestre we rule out blow ups for space homogeneous Landau equation. This result follows from the monotonicity along the flow of an important functional in statistics — the Fisher information. This realization was made possible by a new lifting procedure which (somewhat surprisingly) relates kinetic equations to linear elliptic equations on families of two-dimensional spheres foliating six dimensional Euclidean space. The convexity of the Fisher information functional as well as the symmetries of the equation play an important role in the proof. Time permitting I will also discuss parts of work of Imbert, Silvestre, and Villani, which improves on the Landau result by obtaining new nonlocal functional inequalities that allow them to rule out blow ups for the space homogeneous Boltzmann equation.
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02:30 PM - 03:30 PM
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Convolution estimates for the Boltzmann gain operator with hard spheres
Ioakeim Ampatzoglou (CUNY, Graduate Center)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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We will discuss new moment-preserving polynomially weighted convolution estimates for the gain operator of the Boltzmann equation with hard potentials, including the critical case of hard-spheres. Our approach relies crucially on a novel cancellation mechanism dealing with the pathological case of energy-absorbing collisions (that is, collisions that accumulate energy to only one of the outgoing particles). These collisions distinguish hard potentials from Maxwell molecules. Our method quantifies the heuristic that, while energy-absorbing collisions occur with non-trivial probability, they are statistically rare, and therefore do not affect the overall averaging behavior of the gain operator. At the technical level, our proof relies solely on tools from kinetic theory, such as geometric identities and angular averaging.
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03:30 PM - 04:00 PM
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Afternoon Tea
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- Location
- SLMath: Atrium
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04:00 PM - 05:00 PM
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Transport and diffusion of high frequency waves in random media: speckle formation and the Gaussian conjecture
Anjali Nair (University of Chicago)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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A well-known conjecture in physical literature states that high frequency waves propagating over long distances through turbulence eventually follows Gaussian statistics, with the second moment following a transport equation. The intensity of such wave fields then follows an exponential law, consistent with speckle formation observed in physical experiments. Though fairly well-accepted and intuitive, this conjecture is not entirely supported by any detailed mathematical derivation. In this talk, I will discuss some recent results demonstrating the Gaussian conjecture in a weak-coupling regime of the paraxial approximation.
The paraxial approximation is a high frequency approximation of the Helmholtz equation, where backscattering is ignored. This takes the form of a Schrödinger equation with a random potential and is often used to model laser propagation through turbulence. I will describe two scaling regimes, one is a kinetic scaling where the second moment is given by a transport equation and a second diffusive scaling, where the second moment follows an anomalous diffusion. In both cases, the limiting Gaussian distribution is fully characterized by its first and second moments.
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Oct 24, 2025
Friday
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09:30 AM - 10:30 AM
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The Fuzzy Landau equation and the Fisher information
Maria Pia Gualdani (The University of Texas at Austin )
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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10:30 AM - 10:40 AM
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Group Photo
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- Location
- SLMath: Front Courtyard
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10:40 AM - 11:00 AM
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Break
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- Location
- SLMath: Atrium
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11:00 AM - 12:00 PM
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Decay and regularity estimates for the relativistic Landau equation
Maja Taskovic (Emory University)
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- SLMath: Eisenbud Auditorium, Online/Virtual
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In 1936 Landau introduced a modification of the Boltzmann equation – the Landau equation – which models a dilute hot plasma in which particles undergo Coulomb interactions. When particle velocities are close to the speed of light, which happens frequently in hot plasmas, effects of Einstein’s theory of special relativity become important. These effects are captured by the relativistic Landau equation, which was introduced by Budker and Beliaev in 1956.
In this talk we will discuss recent results for the spatially inhomogeneous relativistic Landau equation in the far-from-equilibrium regime, including regularity and decay estimates. Some of the challenges in the analysis of the relativistic Landau equation come from the complex structure of the collision operator and the lack of scaling symmetries enjoyed by the classical counterpart. We will discuss tools and strategies that helped us overcome difficulties caused by the relativistic setting.
The talk is based on joint works with Henderson, Snelson and Tarfulea, and with Strain.
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12:00 PM - 01:30 PM
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Lunch
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01:30 PM - 02:30 PM
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Quantum Stochastic Gradient Descent in its continuous-time limit based on the Wigner formulation of Open Quantum Systems
Jose Morales Escalante (University of Texas at san Antonio)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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This project is related to the development of quantum algorithms for stochastic gradient descent. These algorithms will be based on the Wigner formulation of quantum mechanics, specifically related to open quantum systems, and with a quantum version of a stochastic equation as the continuous limit of a stochastic iteration. Thus, utilizing the Wigner-Fokker-Planck equation for quantum transport as the fundamental model under Markovian noise. By using previous estimates by Sparber et al., we study the dependance of the exponential convergence rate as a function of the problem dimensionality for the benchmark case of a harmonic potential.
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02:30 PM - 03:30 PM
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A numerical methodists adventure in the land of quantum computing
Daniel Appelo (Virginia Polytechnic Institute and State University)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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In this talk we will: 1. Introducing the most basic concepts in quantum computing. 2. Briefly highlight examples of quantum algorithms where the skills of an (numerical) analyst can be of use. 3. Describe one type of quantum computing hardware (a transmon) and how it is modeled. 4. Time permitting, we then turn to the simulation of open quantum systems and show how to design high order accurate methods that exploit low rank structure in the density matrix while respecting the essential structure of the Lindblad equation. Our methods preserve complete positivity and are trace preserving.
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03:30 PM - 04:00 PM
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Afternoon Tea
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- Location
- SLMath: Atrium
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04:00 PM - 05:00 PM
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Kinetic theory and the long-time behavior of boundary driven quantum systems near the Zeno limit
Eric Carlen (Rutgers University)
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- Location
- SLMath: Eisenbud Auditorium, Online/Virtual
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Abstract
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