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- All Subjects: Computer Science
- All Subjects: Simulation
Description
Polar ice masses can be valuable indicators of trends in global climate. In an effort to better understand the dynamics of Arctic ice, this project analyzes sea ice concentration anomaly data collected over gridded regions (cells) and builds graphs based upon high correlations between cells. These graphs offer the opportunity to use metrics such as clustering coefficients and connected components to isolate representative trends in ice masses. Based upon this analysis, the structure of sea ice graphs differs at a statistically significant level from random graphs, and several regions show erratically decreasing trends in sea ice concentration.
ContributorsWallace-Patterson, Chloe Rae (Author) / Syrotiuk, Violet (Thesis director) / Colbourn, Charles (Committee member) / Montgomery, Douglas (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
Complex systems appear when interaction among system components creates emergent behavior that is difficult to be predicted from component properties. The growth of Internet of Things (IoT) and embedded technology has increased complexity across several sectors (e.g., automotive, aerospace, agriculture, city infrastructures, home technologies, healthcare) where the paradigm of cyber-physical systems (CPSs) has become a standard. While CPS enables unprecedented capabilities, it raises new challenges in system design, certification, control, and verification. When optimizing system performance computationally expensive simulation tools are often required, and search algorithms that sequentially interrogate a simulator to learn promising solutions are in great demand. This class of algorithms are black-box optimization techniques. However, the generality that makes black-box optimization desirable also causes computational efficiency difficulties when applied real problems. This thesis focuses on Bayesian optimization, a prominent black-box optimization family, and proposes new principles, translated in implementable algorithms, to scale Bayesian optimization to highly expensive, large scale problems. Four problem contexts are studied and approaches are proposed for practically applying Bayesian optimization concepts, namely: (1) increasing sample efficiency of a highly expensive simulator in the presence of other sources of information, where multi-fidelity optimization is used to leverage complementary information sources; (2) accelerating global optimization in the presence of local searches by avoiding over-exploitation with adaptive restart behavior; (3) scaling optimization to high dimensional input spaces by integrating Game theoretic mechanisms with traditional techniques; (4) accelerating optimization by embedding function structure when the reward function is a minimum of several functions. In the first context this thesis produces two multi-fidelity algorithms, a sample driven and model driven approach, and is implemented to optimize a serial production line; in the second context the Stochastic Optimization with Adaptive Restart (SOAR) framework is produced and analyzed with multiple applications to CPS falsification problems; in the third context the Bayesian optimization with sample fictitious play (BOFiP) algorithm is developed with an implementation in high-dimensional neural network training; in the last problem context the minimum surrogate optimization (MSO) framework is produced and combined with both Bayesian optimization and the SOAR framework with applications in simultaneous falsification of multiple CPS requirements.
ContributorsMathesen, Logan (Author) / Pedrielli, Giulia (Thesis advisor) / Candan, Kasim (Committee member) / Fainekos, Georgios (Committee member) / Gel, Esma (Committee member) / Montgomery, Douglas (Committee member) / Zabinsky, Zelda (Committee member) / Arizona State University (Publisher)
Created2021
Description
Woodland/Alloy Casting, Inc. is an aluminum foundry known for providing high-quality molds to their customers in industries such as aviation, electrical, defense, and nuclear power. However, as the company has grown larger during the past three years, they have begun to struggle with the on-time delivery of their orders. Woodland prides itself on their high-grade process that includes core processing, the molding process, cleaning process, and heat-treat process. To create each mold, it has to flow through each part of the system flawlessly. Throughout this process, significant bottlenecks occur that limit the number of molds leaving the system. To combat this issue, this project uses a simulation of the foundry to test how best to schedule their work to optimize the use of their resources. Simulation can be an effective tool when testing for improvements in systems where making changes to the physical system is too expensive. ARENA is a simulation tool that allows for manipulation of resources and process while also allowing both random and selected schedules to be run through the foundry’s production process. By using an ARENA simulation to test different scheduling techniques, the risk of missing production runs is minimized during the experimental period so that many different options can be tested to see how they will affect the production line. In this project, several feasible scheduling techniques are compared in simulation to determine which schedules allow for the highest number of molds to be completed.
ContributorsAdams, Danielle Renee (Author) / Pavlic, Theodore (Thesis director) / Montgomery, Douglas (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05