Joint work with Doug Oldenburg.Image appraisal in geophysical inverse problem can provide insight into theresolving capability and uncertainty of estimates. Although a rigorous approachto solve nonlinear appraisal analysis is still lacking but several methods havebeen proposed in the past such as linearized Backus-Gilbert analysis, funnelfunctio...

Creator:

Routh, Partha S. (Boise State University)

Created:

2005-10-18

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

I shall talk about some practical problems of special effects /animation production that could be broadly defined as Inverse Problems:data is recovered from observed images rather than generated on userspecification. Most of Computer Vision tasks in movie production,opposite to most Computer Graphics tasks, fall in Inverse Problemscategory. The ...

Creator:

Vendrovsky, Eugene (Rhythm & Hues Studios, Inc.)

Created:

2006-02-09

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

We investigate the feasibility of quantifying properties of a composite dielectric material through the reflectance, where the permittivity is described by the Lorentz model in which an unknown probability measure is placed on the model parameters. We summarize the computational and theoretical framework (the Prohorov Metric Framework) developed...

Creator:

Banks, Harvey Thomas (North Carolina State University)

Created:

2016-03-15

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

In this talk, I will present an overview of sparse representations and their applications in solving inverse modeling problems involving PDEs that describe multi-phase flow in heterogeneous porous media. The related PDE-constrained inverse problems are often formulated to infer spatially distributed material properties from dynamic response meas...

Creator:

Jafarpour, Benham (University of Southern California)

Created:

2016-06-10

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

A Generative Adversarial Network (GAN) trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed inverse problems. We describe an approach along these lines, with some modifications and refinements, with the following features: (1) on a given class of images, it addresses different linear...

Creator:

Bresler, Yoram (University of Illinois at Urbana-Champaign)

Created:

2019-10-16

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

In the talk I will present recent results on multifrequency electrical impedance tomography. The inverse problem consists in identifying a conductivity inclusion inside a homogeneous background medium by injecting one current. I will use an original spectral decomposition of the solution of the forward conductivity problem to retrieve the Cauchy...

Creator:

Triki, Faouzi (Université Grenoble-Alpes)

Created:

2017-02-16

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

Optical coherence tomography (OCT) provides an alternative to physical sectioning that allows for imaging of living samples and even in vivo examination of cell structure and dynamics. There is, in the OCT community, a widely held belief that there exists a trade-off between transverse resolution and the thickness of the volume that may be image...

Creator:

Carney, P. Scott (University of Illinois at Urbana-Champaign)

Created:

2017-02-14

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

The standard problem of optical tomography is to obtain information about the optical properties of an object by making measurements on the boundary. Acousto-optic tomography is a variation of this problem where the object is perturbed by an acoustic field, and optical boundary measurements are taken as the parameters of the acoustic field vary....

Creator:

Chung, Francis (University of Kentucky)

Created:

2017-02-16

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

In this talk, we will review some key inverse problems in medical imaging and vision, in particular, segmentation and registration. The approach will be via energy minimization through the calculus of variations as well Bayesian. We will also show how these techniques made be made interactive through the use of feedback. This will allow us to ma...

Creator:

Tannenbaum, Allen (State University of New York, Stony Brook (SUNY))

Created:

2016-03-15

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

Sparse decomposition methods are effective tools in a myriad biomedical inverse problems. However, in many settings reconstruction is only an intermediate goal preceding additional quantitative analysis. For instance, we may wish to classify tissue types in microscope images or identify tumors or lesions based on computed tomography data. This t...

Creator:

Willett Lu, Rebecca (Duke University)

Created:

2011-11-16

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

We consider a very general inverse problem on directed graphs.Surprisingly, this problem can actually be solved, explicitly, in a largeclass of examples. I will describe the construction of these examples, aswell as the method used to produce the inversion formulas. This is jointwork with F. Alberto Grunbaum.

Creator:

Matusevich, Laura Felicia (Texas A & M University)

Created:

2005-11-10

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

Since in Bayesian inversion data are often informative only on a low-dimensional subspace of the parameter space,significant computational savings can be achieved using such subspace to characterize and approximate the posterior distribution of the parameters. We study approximations of the posterior covariance matrix defined as low-rank updates...

Creator:

Tenorio, Luis (Colorado School of Mines)

Created:

2017-09-08

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

Parameter identification problems typically consist of a model equation, e.g. a (system of) ordinary or partial differential equation(s), and the observation equation. In the conventional reduced setting, the model equation is eliminated via the parameter-to-state map. Alternatively, one might consider both sets of equations (model and observati...

Creator:

Kaltenbacher, Barbara (Universität Klagenfurt)

Created:

2016-03-15

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

In this talk, I will provide an introduction to the use of machine learning and convolutional neural networks (CNNs) in the area of MR image reconstruction. Building on a general framework of inverse problems and variational optimization, I will focus on application examples from image reconstruction for accelerated Magnetic Resonance (MR) imagi...

Creator:

Knoll, Florian (NYU Langone Medical Center)

Created:

2019-10-17

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

The subsurface is where most of the available freshwater is stored; in the United States, groundwater is the primary source of water for over 50 percent of Americans, and roughly 95 percent for those in rural areas. Cleaning up the surface from industrial and nuclear wastes is quite challenging. A major impediment in studying processes in the su...

Creator:

Kitanidis, Peter K. (Stanford University)

Created:

2011-04-13

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

Many inverse problems may involve a large number of observations. Yet these observations are seldom equally informative; moreover, practical constraints on storage, communication, and computational costs may limit the number of observations that one wishes to employ. We introduce strategies for selecting subsets of the data that yield accurate a...

Creator:

Marzouk, Youssef (Massachusetts Institute of Technology)

Created:

2017-09-06

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

Deducing the state or structure of a system from partial, noisy measurements is a fundamental task throughout the sciences and engineering. The resulting inverse problems are often ill-posed because there are fewer measurements available than the ambient dimension of the model to be estimated. In practice, however, many interesting signals or mo...

Creator:

Chandrasekaran, Venkat (University of California, Berkeley)

Created:

2012-03-26

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

I will discuss the inverse problem of inferring the Green's function for advective-diffusive transport (also known as the transit-time distribution) from tracer observations. Tracers with different boundary conditions and/or different radio-active decay rates probe different transport pathways and timescales. Using multiple tracers in combinatio...

Creator:

Primeau, François W. (University of California, Irvine)

Created:

2010-04-12

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

In this talk we examine a class of inverse problems that arise ongraphs. We provide a review ofrecent developments, including design aspects for identifiabilitypurposes, inference issues andapplications to computer networks.

Creator:

Michailidis, George (University of Michigan)

Created:

2005-11-10

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

In inverse boundary problems one attempts to determine the properties of amedium by making measurements at the boundary of the medium. In thelecture we will concentrate on two inverse boundary problems, ElectricalImpedance Tomography and Travel Tomography in anisotropic media. Theseproblems arise in medical imaging, geophysics and other fields. ...

Creator:

Uhlmann, Gunther A. (University of Washington)

Created:

2005-10-19

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

1. Introduction and Motivation-What are Inverse problems?-Examples: detection of contaminant sources, image and voice recognition, medical imaging, subsurface imaging, materials identification2. Theoretical aspects of (discrete) inverse problems-Why are inverse problems (oftentimes) difficult to solve?-Well-posed and ill-posed problems: existenc...

Creator:

Aquino, Wilkins (Duke University)

Created:

2016-06-07

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.

Inverse problems are often formulated as constrained optimization problems. The constraint is in the form of a 'forward' problem. The forward model is repeatedly solved and iteratively updated in order to bring its predictions in conformity with a set of data, or observations. We identify two distinct classes of deficiencies present in standard ...

Creator:

Barbone, Paul E. (Boston University)

Created:

2016-06-09

Contributed By:

University of Minnesota, Institute for Mathematics and its Applications.