Path-based splitting methods for SDEs and machine learning for battery lifetime prognostics

In the first half of this Thesis, we present the numerical analysis of splitting methods for stochastic differential equations (SDEs) using a novel path-based approach. The application of splitting methods to SDEs can be viewed as replacing the driving Brownian-time path with a piecewise linear path, producing a ‘controlled-differential-equation’ (CDE). By Taylor expansion of the SDE and resulting CDE, we show that the global strong and weak errors of splitting schemes can be obtained by comparison of the iterated integrals in each. Matching all integrals up to order p+1 in expectation will produce a weak order p+0.5 scheme, and in addition matching the integrals up to order p+0.5 strongly will produce a strong order p scheme. In addition, we present new splitting methods utilising the ‘space-time’ L´evy area of Brownian motion which obtain global strong Oph1.5q and Oph2q weak errors for a class of SDEs satisfying a commutativity condition. We then present several numerical examples including Multilevel Monte Carlo. In the second half of this Thesis, we present a series of papers focusing on lifetime prognostics for lithium-ion batteries. Lithium-ion batteries are fuelling the advancing renewable-energy based world. At the core of transformational developments in battery design, modelling and management is data. We start with a comprehensive review of publicly available datasets. This is followed by a study which explores the evolution of internal resistance (IR) in cells, introducing the original concept of ‘elbows’ for IR. The IR of cells increases as a cell degrades and this often happens in a non-linear fashion: where early degradation is linear until an inflection point (the elbow) is reached followed by increased rapid degradation. As a follow up to the exploration of IR, we present a model able to predict the full IR and capacity evolution of a cell from one charge/discharge cycle. At the time of publication, this represented a significant reduction (100x) in the number of cycles required for prediction. The published paper was the first to show that such results were possible. In the final paper, we consider experimental design for battery testing. Where we focus on the important question of how many cells are required to accurately capture statistical variation

Recent tendencies in the use of optimization techniques in geotechnics:a review

The use of optimization methods in geotechnics dates back to the 1950s. They were used in slope stability analysis (Bishop) and evolved to a wide range of applications in ground engineering. We present here a non-exhaustive review of recent publications that relate to the use of different optimization techniques in geotechnical engineering. Metaheuristic methods are present in almost all the problems in geotechnics that deal with optimization. In a number of cases, they are used as single techniques, in others in combination with other approaches, and in a number of situations as hybrids. Different results are discussed showing the advantages and issues of the techniques used. Computational time is one of the issues, as well as the assumptions those methods are based on. The article can be read as an update regarding the recent tendencies in the use of optimization techniques in geotechnics

Whitworth University Catalog 2017-2018

https://digitalcommons.whitworth.edu/whitworthcatalogs/1095/thumbnail.jp

Whitworth University Catalog 2020-2021

https://digitalcommons.whitworth.edu/whitworthcatalogs/1098/thumbnail.jp

Whitworth University Catalog 2018-2019

https://digitalcommons.whitworth.edu/whitworthcatalogs/1096/thumbnail.jp

Big Data and Climate Change

open access articleClimate science as a data-intensive subject has overwhelmingly affected by the era of big data and relevant technological revolutions. The big successes of big data analytics in diverse areas over the past decade have also prompted the expectation of big data and its efficacy on the big problem—climate change. As an emerging topic, climate change has been at the forefront of the big climate data analytics implementations and exhaustive research have been carried out covering a variety of topics. This paper aims to present an outlook of big data in climate change studies over the recent years by investigating and summarising the current status of big data applications in climate change related studies. It is also expected to serve as a one-stop reference directory for researchers and stakeholders with an overview of this trending subject at a glance, which can be useful in guiding future research and improvements in the exploitation of big climate data

Whitworth University Catalog 2019-2020

https://digitalcommons.whitworth.edu/whitworthcatalogs/1097/thumbnail.jp

Whitworth University Catalog 2021-2022

https://digitalcommons.whitworth.edu/whitworthcatalogs/1099/thumbnail.jp

Gaussian Process-based Optimization using Mutual Information for Computer Experiments. Application to Storm Surge extremes

The computational burden of running a complex computer model can make optimization impractical. Gaussian Processes (GPs) are statistical surrogates (also known as emulators) that alleviate this issue since they cheaply replace the computer model. As a result, the exploration vs. exploitation trade-off strategy can be accelerated by building a GP surrogate. In the current study, we propose a new surrogate-based optimization scheme that minimizes the number of evaluations of the computationally expensive function. Taking advantage of parallelism of the evaluation of the unknown function, the uncertain regions are explored simultaneously, and a batch of input points is chosen using Mutual Information for Computer Experiments (MICE), a sequential design algorithm which maximizes the in- formation theoretic Mutual Information over the input space. The computational efficiency of interweaving the optimization scheme with MICE (optim-MICE) is examined and demonstrated on test functions. Optim-MICE is compared with state- of-the-art heuristics. We demonstrate that optim-MICE outperforms the alternative schemes on a large range of computational experiments. The proposed algorithm is also employed to study the extrema of coastal storm waves, such as the ones that ob- served during Typhoon Haiyan (2013, Philippines). A stretch of coral reef near the coast, which was expected to protect the coastal communities, actually amplified the waves. The propagation and breaking process of such large nearshore waves can be successfully captured by a phase-resolving wave model. However, the computational complexity of the simulator makes optimization tasks impractical. The optim-MICE algorithm is therefore used to find the maximum breaking wave (bore) height and the maximum run-up. In two idealised settings, we efficiently identify the conditions that create the largest storm waves at the coast using a minimal number of simulations. This is the first surrogate-based optimization of storm waves and it opens the door to previously inconceivable coastal risk assessments

E-mentoring in Online Programming Communities : Opportunities, Challenges, Activities and Strategies

Mentoring is known to effectively improve professional development. The advancements in Information Technology area have positively impacted the process of mentoring through a more technology-mediated form of mentoring known as e-mentoring or online mentoring. Online mentoring had a particularly strong effect in improving the learning opportunities in online programming communities where mentees and mentors interact with each other from around the world in a mutually beneficial learning experience and collaboration. Yet, the lack of a coherent understanding of different characteristics (e.g., opportunities, challenges, activities, and strategies employed by mentees and mentors) of e-mentoring in online programming communities and lack of knowledge about mentoring aspects of applying e-mentoring in different types of online programming platforms inhibit us from an informed design or redesign of systems for e-mentoring in such communities. With a specific focus on those shortcomings, this research presents several empirical studies to advance the understanding of e-mentoring in online programming communities. First, we investigate the emerging opportunities and challenges faced by e-mentoring in online programming community. Next, we identify and classify e-mentoring activities carried out in this context. We investigate the strategies employed to overcome e-mentoring challenges in online programming communities. Finally, based on our findings, this dissertation proposes a conceptual framework for augmenting socio-technical systems with e-mentoring. The dissertation also provides comprehensive contributions that enhance the understanding of e-mentoring in online communities and provides improvement recommendations (e.g., encouraging academic members to help by offering their services to online communities as a part of their university work, using chatbots for automated responses to queries, and improving features to manage e-mentoring tasks and projects)