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Matlab for Newbies
Title
Matlab for Newbies: The Bare Essentials.
Creator
Verma, Siddhartha
Abstract/Description
This book will guide your first steps in programming in Matlab, no matter whether you want to learn it for work, fun, or just to satisfy your curiosity! Getting comfortable with the basics of programming in Matlab will be our main goal in this first of several segments that I hope to write. We will focus precisely on the things that you will need to get set up and running. You will be able to interpret simple code, and atleast be able to understand what the code’s author is trying to achieve.
Date Issued
2019
PURL
http://purl.flvc.org/fau/fd/FAUIR000374
Subject Headings
Open Educational Resources
Format
Citation
SUSTAINING CHAOS USING DEEP REINFORCEMENT LEARNING
Title
SUSTAINING CHAOS USING DEEP REINFORCEMENT LEARNING.
Creator
Vashishtha, Sumit, Verma, Siddhartha, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description
Numerous examples arise in fields ranging from mechanics to biology where disappearance of Chaos can be detrimental. Preventing such transient nature of chaos has been proven to be quite challenging. The utility of Reinforcement Learning (RL), which is a specific class of machine learning techniques, in discovering effective control mechanisms in this regard is shown. The autonomous control algorithm is able to prevent the disappearance of chaos in the Lorenz system exhibiting meta-stable...
Show moreNumerous examples arise in fields ranging from mechanics to biology where disappearance of Chaos can be detrimental. Preventing such transient nature of chaos has been proven to be quite challenging. The utility of Reinforcement Learning (RL), which is a specific class of machine learning techniques, in discovering effective control mechanisms in this regard is shown. The autonomous control algorithm is able to prevent the disappearance of chaos in the Lorenz system exhibiting meta-stable chaos, without requiring any a-priori knowledge about the underlying dynamics. The autonomous decisions taken by the RL algorithm are analyzed to understand how the system’s dynamics are impacted. Learning from this analysis, a simple control-law capable of restoring chaotic behavior is formulated. The reverse-engineering approach adopted in this work underlines the immense potential of the techniques used here to discover effective control strategies in complex dynamical systems. The autonomous nature of the learning algorithm makes it applicable to a diverse variety of non-linear systems, and highlights the potential of RLenabled control for regulating other transient-chaos like catastrophic events.
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Date Issued
2020
PURL
http://purl.flvc.org/fau/fd/FA00013498
Subject Headings
Machine learning--Technique, Reinforcement learning, Algorithms, Chaotic behavior in systems, Nonlinear systems
Format
Document (PDF)
On the topic of Aerosol Generation and Propagation
Title
On the topic of Aerosol Generation and Propagation.
Creator
Schreck, Jesse H., Verma, Siddhartha, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description
In this research, three methods of aerosol generation, and their subsequent propagation, are investigated experimentally. These experiments provided insight into the potential risk aerosol can have pertaining to the spread of infectious disease such as COVID – 19. The first of which investigated an alternative generation route that may exist given the discovery of small numbers of viable viruses in urine and stool samples. Flushing biomatter can lead to the aerosolization of micro-organisms;...
Show moreIn this research, three methods of aerosol generation, and their subsequent propagation, are investigated experimentally. These experiments provided insight into the potential risk aerosol can have pertaining to the spread of infectious disease such as COVID – 19. The first of which investigated an alternative generation route that may exist given the discovery of small numbers of viable viruses in urine and stool samples. Flushing biomatter can lead to the aerosolization of micro-organisms; thus, there is a likelihood that bioaerosols generated in public restrooms may pose a concern for the transmission of COVID-19, especially since these areas are relatively confined, experience heavy foot traffic, and may suffer from inadequate ventilation. The results indicate that the particular designs tested in the study generate a large number of droplets in the size range 0.3 𝜇𝑚 – 3 𝜇𝑚, which can reach heights of at least 1.52 m. This highlights the need for incorporating adequate ventilation in the design and operation of public spaces, which can help prevent aerosol accumulation in high occupancy areas and mitigate the risk of airborne disease transmission. Secondly, experiments were conducted to evaluate the effectiveness of facial coverings at various distances around a simulated cough. These concluded that due to the gaps along the seal of a face mask, aerosols can escape 360° around a coughing individual. In the final portion of the thesis study, an experimental method was developed and conducted to break up a droplet via mechanical excitation. The results of these experiments showed that when a droplet is placed on a vibrating string, the droplet can be broken into many secondary droplets which is analogous to one speaking or singing thus providing insight as to how vocal cords can generate respiratory aerosols.
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Date Issued
2021
PURL
http://purl.flvc.org/fau/fd/FA00013685
Subject Headings
Aerosols, Communicable diseases--Transmission, COVID-19
Format
Document (PDF)
DATA-DRIVEN IDENTIFICATION AND CONTROL OF TURBULENT STRUCTURES USING DEEP NEURAL NETWORKS
Title
DATA-DRIVEN IDENTIFICATION AND CONTROL OF TURBULENT STRUCTURES USING DEEP NEURAL NETWORKS.
Creator
Jagodinski, Eric, Verma, Siddhartha, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description
Wall-bounded turbulent flows are pervasive in numerous physics and engineering applications. Such flows tend to have a strong impact on the design of ships, airplanes and rockets, industrial chemical mixing, wind and hydrokinetic energy, utility infrastructure and innumerable other fields. Understanding and controlling wall bounded turbulence has been a long-pursued endeavor yielding plentiful scientific and engineering discoveries, but there is much that remains unexplained from a...
Show moreWall-bounded turbulent flows are pervasive in numerous physics and engineering applications. Such flows tend to have a strong impact on the design of ships, airplanes and rockets, industrial chemical mixing, wind and hydrokinetic energy, utility infrastructure and innumerable other fields. Understanding and controlling wall bounded turbulence has been a long-pursued endeavor yielding plentiful scientific and engineering discoveries, but there is much that remains unexplained from a fundamental viewpoint. One unexplained phenomenon is the formation and impact of coherent structures like the ejections of slow near-wall fluid into faster moving ow which have been shown to correlate with increases in friction drag. This thesis focuses on recognizing and regulating organized structures within wall-bounded turbulent flows using a variety of machine learning techniques to overcome the nonlinear nature of this phenomenon. Deep Learning has provided new avenues of analyzing large amounts of data by applying techniques modeled after biological neurons. These techniques allow for the discovery of nonlinear relationships in massive, complex systems like the data found frequently in fluid dynamics simulation. Using a neural network architecture called Convolutional Neural Networks that specializes in uncovering spatial relationships, a network was trained to estimate the relative intensity of ejection structures within turbulent flow simulation without any a priori knowledge of the underlying flow dynamics. To explore the underlying physics that the trained network might reveal, an interpretation technique called Gradient-based Class Activation Mapping was modified to identify salient regions in the flow field which most influenced the trained network to make an accurate estimation of these organized structures. Using various statistical techniques, these salient regions were found to have a high correlation to ejection structures, and to high positive kinetic energy production, low negative production, and low energy dissipation regions within the flow. Additionally, these techniques present a general framework for identifying nonlinear causal structures in general three-dimensional data in any scientific domain where the underlying physics may be unknown.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00014119
Subject Headings
Turbulent flow, Turbulence, Neural networks (Computer science), Deep learning (Machine learning)
Format
Document (PDF)
EXPLORING UNDULATORY SWIMMING BEHAVIORS WITH DEEP REINFORCEMENT LEARNING
Title
EXPLORING UNDULATORY SWIMMING BEHAVIORS WITH DEEP REINFORCEMENT LEARNING.
Creator
Alvaro, Alejandro, Verma, Siddhartha, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description
The capability to navigate in the proximity of solid surfaces while avoiding collision and maintaining high efficiency is essential for the effective design and operation of underwater vehicles. The underlying capability involves a variety of challenges, and a potential approach to overcome such obstacles is to rely on biomimetic or bio-inspired design. Through evolution, organisms have developed methods of locomotion optimized for their specific environment. One of the common forms of...
Show moreThe capability to navigate in the proximity of solid surfaces while avoiding collision and maintaining high efficiency is essential for the effective design and operation of underwater vehicles. The underlying capability involves a variety of challenges, and a potential approach to overcome such obstacles is to rely on biomimetic or bio-inspired design. Through evolution, organisms have developed methods of locomotion optimized for their specific environment. One of the common forms of locomotion found in underwater organisms is undulatory swimming. These undulatory swimmers display different swimming behaviors based on the flow conditions in their environment. These behaviors take advantage of changes in the flow field caused by the presence of obstructions and obstacles upstream or adjacent to the swimmer. For example, a free swimmer in near-proximity to a flat plane can experience changes in lift and drag during locomotion. The reduced drag can benefit the swimmer, however, changes in lift may lead to a collision with obstacles. Despite the abundance of qualitative data from observing these undulatory swimmers, there is a lack of quantitative data, creating a disconnect in understanding how these organisms have evolved to exploit the presence of walls and obstacles. By employing a combination of traditional computational fluid dynamics and novel neural network-based techniques it is possible to emulate the evolution of learned behavior in biological organisms. The current work uses deep reinforcement learning coupled with two-dimensional numerical simulations of self-propelled swimmers to better understand behavior observed in nature.
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Date Issued
2024
PURL
http://purl.flvc.org/fau/fd/FA00014402
Subject Headings
Reinforcement learning, Computational fluid dynamics, Autonomous underwater vehicles
Format
Document (PDF)
FLOW-MEDIATED NAVIGATION AND COORDINATION OF ARTIFICIAL SWIMMERS USING DEEP REINFORCEMENT LEARNING
Title
FLOW-MEDIATED NAVIGATION AND COORDINATION OF ARTIFICIAL SWIMMERS USING DEEP REINFORCEMENT LEARNING.
Creator
Nair, Aishwarya, Verma, Siddhartha, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description
Aquatic organisms are able to achieve swimming efficiencies that are much higher than any underwater vehicle that has been designed by humans. This is mainly due to the adaptive swimming patterns that they display in response to changes in their environment and their behaviors, i.e., hunting, fleeing, or foraging. In this work, we explore these adaptations from a hydrodynamics standpoint, using numerical simulations to emulate self-propelled artificial swimmers in various flow fields. Apart...
Show moreAquatic organisms are able to achieve swimming efficiencies that are much higher than any underwater vehicle that has been designed by humans. This is mainly due to the adaptive swimming patterns that they display in response to changes in their environment and their behaviors, i.e., hunting, fleeing, or foraging. In this work, we explore these adaptations from a hydrodynamics standpoint, using numerical simulations to emulate self-propelled artificial swimmers in various flow fields. Apart from still or uniform flow, the most likely flow field encountered by swimmers are those formed by the wakes of solid objects, such as roots of aquatic vegetation, or underwater structures. Therefore, a simplified bio-inspired design of porous structures consisting of nine cylinders was considered to identify arrangements that could produce wakes of varying velocities and enstrophy, which in turn might provide beneficial environments for underwater swimmers. These structures were analyzed using a combination of numerical simulations and experiments, and the underlying flow physics was examined using a variety of data-analysis techniques. Subsequently, in order to recreate the adaptations of natural swimmers in different flow regimes, artificial swimmers were positioned in each of these different types of flow fields and then trained to optimize their movements to maximize swimming efficiency using deep reinforcement learning. These artificial swimmers utilize a sensory input system that allows them to detect the velocity field and pressure on the surface of their body, which is similar to the lateral line sensing system in biological fish. The results demonstrate that the information gleaned from the simplified lateral line system was sufficient for the swimmer to replicate naturally found behaviors such as K´arm´an gaiting. The phenomenon of schooling in underwater organisms is similarly thought to provide opportunities for swimmers to increase their energy efficiency, along with the other associated benefits. Thus, multiple swimmers were trained using multi-agent reinforcement learning to discover optimal swimming patterns at the group level as well as the individual level.
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Date Issued
2024
PURL
http://purl.flvc.org/fau/fd/FA00014413
Subject Headings
Reinforcement learning, Hydrodynamics, Computational fluid dynamics, .
Format
Document (PDF)