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DEEP LEARNING IN ENGINEERING MECHANICS: WAVE PROPAGATION AND DYNAMICS IMPLEMENTATIONS
Title
DEEP LEARNING IN ENGINEERING MECHANICS: WAVE PROPAGATION AND DYNAMICS IMPLEMENTATIONS
Creator
Finol Berrueta, David
Date
2019
Description
With the advent of Artificial Intelligence research in the 1960s, the need for intelligent systems that are able to truly comprehend the...
Show moreWith the advent of Artificial Intelligence research in the 1960s, the need for intelligent systems that are able to truly comprehend the physical world around them became relevant. Significant milestones in the realm of machine learning and, in particular, deep learning during the past decade have led to advanced data-driven models that are able to approximate complex functions from pure observations. When it comes to the application of physics-based scenarios, the vast majority of these models rely on statistical and optimization constructs, leaving minimal room in their development for the physics-driven frameworks that more traditional engineering and science fields have been developing for centuries. On the other hand, the more traditional engineering fields, such as mechanics, have evolved on a different set of modeling tools that are mostly based on physics driven assumptions and equations, typically aided by statistical tools for uncertainty handling. Deep learning models can provide significant implementation advantages in commercial systems over traditional engineering modeling tools in the current economies of scale, but they tend to lack the strong reliability their counterparts naturally allow. The work presented in this thesis is aimed at assessing the potential of deep learning tools, such as Convolutional Neural Networks and Long Short-Term Memory Networks, as data-driven models in engineering mechanics, with a major focus on vibration problems. In particular, two implementation cases are presented: a data driven surrogate model to a Phononic eigenvalue problem, and a physics-learning model in rigid-body dynamics scenario. Through the applications presented, this work that shows select deep learning architectures can appropriately approximate complex functions found in engineering mechanics from a system’s time history or state and generalize to set expectations outside training domains. In spatio-temporal systems, it is also that shown local learning windows along space and time can provide improved model reliability in their approximation and generalization performance
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Optimization methods and machine learning model for improved projection of energy market dynamics
Title
Optimization methods and machine learning model for improved projection of energy market dynamics
Creator
Saafi, Mohamed Ali
Date
2023
Description
Since signing the legally binding Paris agreement, governments have been striving to fulfill the decarbonization mission. To reduce carbon...
Show moreSince signing the legally binding Paris agreement, governments have been striving to fulfill the decarbonization mission. To reduce carbon emissions from the transportation sector, countries around the world have created a well-defined new energy vehicle development strategy that is further expanding into hydrogen vehicle technologies. In this study, we develop the Transportation Energy Analysis Model (TEAM) to investigate the impact of the CO2 emissions policies on the future of the automotive industries. On the demand side, TEAM models the consumer choice considering the impacts of technology cost, energy cost, refueling/charging availability, consumer travel pattern. On the supply side, the module simulates the technology supply by the auto-industry with the objective of maximizing industry profit under the constraints of government policies. Therefore, we apply different optimization methods to guarantee reaching the optimal automotive industry response each year up to 2050. From developing an upgraded differential evolution algorithm, to applying response surface methodology to simply the objective function, the goal is to enhance the optimization performance and efficiency compared to adopting the standard genetic algorithm. Moreover, we investigate TEAM’s robustness by applying a sensitivity analysis to find the key parameters of the model. Finally based on the key sensitive parameters that drive the automotive industry, we develop a neural network to learn the market penetration model and predict the market shares in a competitive time by bypassing the total cost of ownership analysis and profit optimization. The central motivating hypothesis of this thesis is that modern optimization and modeling methods can be applied to obtain a computationally-efficient, industry-relevant model to predict optimal market sales shares for light-duty vehicle technologies. In fact, developing a robust market penetration model that is optimized using sophisticated methods is a crucial tool to automotive companies, as it quantifies consumer’s behavior and delivers the optimal way to maximize their profits by highlighting the vehicles technologies that they could invest in. In this work, we prove that TEAM reaches the global solution to optimize not only the industry profits but also the alternative fuels optimized blends such as synthetic fuels. The time complexity of the model has been substantially improved to decrease from hours using the genetic algorithm, to minutes using differential evolution, to milliseconds using neural network.
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