Nowhere people: making the invisible, visible

This context statement presents a critical examination of my work as a documentary photographer, focusing on the long-term project Nowhere People (2005-2016). It reveals as well the impact statelessness has on individuals and communities around the world. Given the interdisciplinary nature of this topic, the statement will employ methods of auto-ethnography as well as theories of state, human rights, memory and identity in order to illustrate the significance of the visual in articulating the lives of stateless populations. This statement attempts to connect my own experience and journey of working in the field of documentary photography with the growing discussion and debate related to the state, human rights, nationality and the rights of non-citizens. I will demonstrate how this portfolio of public works presents a multidimensional portrait of this issue in a way that contributes to filling in substantial evidence gaps in the context of human rights by making the various elements of this invisible condition, visible. In addition, I will survey photography’s capacity to translate deficiencies inherent within human rights and international law and examine its role in challenging contemporary definitions of citizenship and identity. I will discuss how I have navigated my work and the project Nowhere People through debates related to subjectivity, agency, representation, spectatorship and the interconnectedness between photographer, subject and viewer. I will also detail the pragmatic decisions I have made relating to the authorship and dissemination of the work to various audiences in relation to the tectonic shift in the access to information and use of the image within this shifting paradigm of visual culture

Residual Minimizing Model Interpolation for Parameterized Nonlinear Dynamical Systems

We present a method for approximating the solution of a parameterized, nonlinear dynamical system using an affine combination of solutions computed at other points in the input parameter space. The coefficients of the affine combination are computed with a nonlinear least squares procedure that minimizes the residual of the governing equations. The approximation properties of this residual minimizing scheme are comparable to existing reduced basis and POD-Galerkin model reduction methods, but its implementation requires only independent evaluations of the nonlinear forcing function. It is particularly appropriate when one wishes to approximate the states at a few points in time without time marching from the initial conditions. We prove some interesting characteristics of the scheme including an interpolatory property, and we present heuristics for mitigating the effects of the ill-conditioning and reducing the overall cost of the method. We apply the method to representative numerical examples from kinetics - a three state system with one parameter controlling the stiffness - and conductive heat transfer - a nonlinear parabolic PDE with a random field model for the thermal conductivity.Comment: 28 pages, 8 figures, 2 table

Discovering an active subspace in a single-diode solar cell model

Predictions from science and engineering models depend on the values of the model's input parameters. As the number of parameters increases, algorithmic parameter studies like optimization or uncertainty quantification require many more model evaluations. One way to combat this curse of dimensionality is to seek an alternative parameterization with fewer variables that produces comparable predictions. The active subspace is a low-dimensional linear subspace defined by important directions in the model's input space; input perturbations along these directions change the model's prediction more, on average, than perturbations orthogonal to the important directions. We describe a method for checking if a model admits an exploitable active subspace, and we apply this method to a single-diode solar cell model with five input parameters. We find that the maximum power of the solar cell has a dominant one-dimensional active subspace, which enables us to perform thorough parameter studies in one dimension instead of five

Factorizing the Stochastic Galerkin System

Recent work has explored solver strategies for the linear system of equations arising from a spectral Galerkin approximation of the solution of PDEs with parameterized (or stochastic) inputs. We consider the related problem of a matrix equation whose matrix and right hand side depend on a set of parameters (e.g. a PDE with stochastic inputs semidiscretized in space) and examine the linear system arising from a similar Galerkin approximation of the solution. We derive a useful factorization of this system of equations, which yields bounds on the eigenvalues, clues to preconditioning, and a flexible implementation method for a wide array of problems. We complement this analysis with (i) a numerical study of preconditioners on a standard elliptic PDE test problem and (ii) a fluids application using existing CFD codes; the MATLAB codes used in the numerical studies are available online.Comment: 13 pages, 4 figures, 2 table