In the current work we present two generalizations of the Parallel Tempering algorithm in the context of discrete-timeMarkov chainMonteCarlo methods for Bayesian inverse problems. These generalizations use state-dependent swapping rates, inspired by the so ...
Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems. These formulations, however, can be arbitrarily difficult to solve in general, in the s ...
Adaptive Optics (AO) improves the efficiency of the optical devices in confocal imaging systems by reducing wavefront aberrations. Aberration is caused by imperfections within the system and reduces the optical signal to noise ratio of the resultant images ...
This work considers sampled data of continuous-domain Gaussian processes. We derive a maximum-likelihood estimator for identifying autoregressive moving average parameters while incorporating the sampling process into the problem formulation. The proposed ...
Tensegrity structures are lightweight structures composed of cables in tension and struts in compression. Since tensegrity systems exhibit geometrically nonlinear behavior, finding optimal structural designs is difficult. This paper focuses on the use of s ...
In this thesis, we focus on standard classes of problems in numerical optimization: unconstrained nonlinear optimization as well as systems of nonlinear equations. More precisely, we consider two types of unconstrained nonlinear optimization problems. On t ...
The aim of this paper is to present a global approach to dynamic optimization of batch emulsion polymerization reactors using a stochastic optimizer. The objective is to minimize the final batch time with constraints on the final conversion and molecular w ...
