Now showing items 1-3 of 3
Optimization with Discrete Simultaneous Perturbation Stochastic Approximation Using Noisy Loss Function Measurements
(Johns Hopkins University, 2013-10-21)
Discrete stochastic optimization considers the problem of minimizing (or maximizing) loss functions defined on discrete sets, where only noisy measurements of the loss functions are available. The discrete stochastic ...
SCALAR-SOURCE IDENTIFICATION AND OPTIMAL SENSOR PLACEMENT IN TURBULENT CHANNEL FLOW
(Johns Hopkins University, 2020-08-17)
The spreading of a released pollutant in a turbulent environment has severe consequences. The ability to identify the unknown source location from remote sensor data is greatly obfuscated by turbulence. This work discusses ...
System Dynamics and Machine Learning Techniques for Studying Resilience in Public Health
(Johns Hopkins University, 2021-01-12)
Systems Dynamics (SD) and Machine Learning (ML) are analytical methods that are becoming more broadly applied to studies in public health. This dissertation focuses on public health aspects of resilience, with an emphasis ...