Physical Sciences and Mathematics Commons^™

Faster, Cheaper, And Better Cfd: A Case For Machine Learning To Augment Reynolds-Averaged Navier-Stokes, John Peter Romano Ii

Mechanical & Aerospace Engineering Theses & Dissertations

In recent years, the field of machine learning (ML) has made significant advances, particularly through applying deep learning (DL) algorithms and artificial intelligence (AI). The literature shows several ways that ML may enhance the power of computational fluid dynamics (CFD) to improve its solution accuracy, reduce the needed computational resources and reduce overall simulation cost. ML techniques have also expanded the understanding of underlying flow physics and improved data capture from experimental fluid dynamics.

This dissertation presents an in-depth literature review and discusses ways the field of fluid dynamics has leveraged ML modeling to date. The author selects and describes …

Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu

Applied Mathematics & Information Sciences

In our previous work, we introduced a clustering algorithm based on clique formation. Cliques, the obtained clusters, are constructed by choosing the most dense complete subgraphs by using similarity values between instances. The clique algorithm successfully reduces the number of instances in a data set without substantially changing the accuracy rate. In this current work, we focused on reducing the number of features. For this purpose, the effect of the clique clustering algorithm on dimensionality reduction has been analyzed. We propose a novel algorithm for support vector machine classification by combining these two techniques and applying different strategies by differentiating …

The Influence Of Allostery Governing The Changes In Protein Dynamics Upon Substitution, Joseph Hess

All Dissertations

The focus of this research is to investigate the effects of allostery on the function/activity of an enzyme, human immunodeficiency virus type 1 (HIV-1) protease, using well-defined statistical analyses of the dynamic changes of the protein and variants with unique single point substitutions ¹. The experimental data¹ evaluated here only characterized HIV-1 protease with one of its potential target substrates. Probing the dynamic interactions of the residues of an enzyme and its variants can offer insight of the developmental importance for allosteric signaling and their connection to a protein’s function. The realignment of the secondary structure elements can …

Using Deep Neural Networks To Classify Astronomical Images, Andrew D. Macpherson

Honors Projects

As the quantity of astronomical data available continues to exceed the resources available for analysis, recent advances in artificial intelligence encourage the development of automated classification tools. This paper lays out a framework for constructing a deep neural network capable of classifying individual astronomical images by describing techniques to extract and label these objects from large images.

U-No: U-Shaped Neural Operators, Md Ashiqur Rahman, Zachary E Ross, Kamyar Azizzadenesheli

Department of Computer Science Faculty Publications

Neural operators generalize classical neural networks to maps between infinite-dimensional spaces, e.g., function spaces. Prior works on neural operators proposed a series of novel methods to learn such maps and demonstrated unprecedented success in learning solution operators of partial differential equations. Due to their close proximity to fully connected architectures, these models mainly suffer from high memory usage and are generally limited to shallow deep learning models. In this paper, we propose U-shaped Neural Operator (U-NO), a U-shaped memory enhanced architecture that allows for deeper neural operators. U-NOs exploit the problem structures in function predictions and demonstrate fast training, data …

A Quantum Approach To Language Modeling, Constantijn Van Der Poel

Dissertations, Theses, and Capstone Projects

This dissertation consists of six chapters. . . Chapter 1: We introduce language modeling, outline the software used for this thesis, and discuss related work. Chapter 2: We will unpack the transition from classical to quantum probabilities, as well as motivate their use in building a model to understand language-like datasets. Chapter 3: We motivate the Motzkin dataset, the models we will be investigating, as well as the necessary algorithms to do calculations with them. Chapter 4: We investigate our models’ sensitivity to various hyperparameters. Chapter 5: We compare the performance and robustness of the models. Chapter 6: We conclude …

Quantum Computing And Its Applications In Healthcare, Vu Giang

OUR Journal: ODU Undergraduate Research Journal

This paper serves as a review of the state of quantum computing and its application in healthcare. The various avenues for how quantum computing can be applied to healthcare is discussed here along with the conversation about the limitations of the technology. With more and more efforts put into the development of these computers, its future is promising with the endeavors of furthering healthcare and various other industries.

Patch-Wise Training With Convolutional Neural Networks To Synthetically Upscale Cfd Simulations, John P. Romano, Alec C. Brodeur, Oktay Baysal

Mechanical & Aerospace Engineering Faculty Publications

This paper expands the authors’ prior work[1], which focuses on developing a convolutional neural network (CNN) model capable of mapping time-averaged, unsteady Reynold’s-averaged Navier-Stokes (URANS) simulations to higher resolution results informed by time-averaged detached eddy simulations (DES). The authors present improvements over the prior CNN autoencoder model that result from hyperparameter optimization, increased data set augmentation through the adoption of a patch-wise training approach, and the predictions of primitive variables rather than vorticity magnitude. The training of the CNN model developed in this study uses the same URANS and DES simulations of a transonic flow around several NACA 4-digit airfoils …

Using Machine Learning To Search For Vector Boson Scattering At The Cms Detector During Run 2, Mark Mekosh

Graduate Research Theses & Dissertations

This work reports on the use of different machine learning (ML) techniques in the search for vector boson scattering (VBS) events in the semileptonic $WV$ channel. VBS is an important process for studying electroweak symmetry breaking (EWSB), the Higgs mechanism, as well as for probing beyond the standard model physics. Boosted decision trees as well as deep neural networks were trained on Monte Carlo simulation samples and applied to 137 fb$^{-1}$ of proton-proton collision data taken from 2016 to 2018 by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) with a center of mass energy $\sqrt{s} …

Using Machine Learning To Predict Student Outcomes, Saba Fatima

Graduate Research Theses & Dissertations

Predicting students’ performance to identify which students are at risk of receiving aD/Fail/Withdraw (DFW) grade and ensuring their timely graduation is not just desirable but also necessary in most educational entities. In the US, not only is the Science, Technology, Engineering, and Mathematics (STEM) major becoming less popular among students, the graduation rate of STEM students is steadily declining. The lack of STEM graduates in the US is a serious problem that will place this country at a disadvantage as a competitor in international technological advancement. In order to secure its status as a technological leader internationally, the US institutions …

Machine Learning Strategies For Potential Development In High-Entropy Driven Nickel-Based Superalloys, Marium Mostafiz Mou

MSU Graduate Theses

In this study, I developed Deep Learning interatomic potentials to model a multi-phase and multi-component system of Ni-based Superalloys. The system has up to three major phase constituents, namely Gamma, Gamma Prime, and Transition-metal rich Carbide. I utilized invariant scalar-based and/or equivariant, tensor-based neural network (NN) approach as implemented in DEEPMD, NEQUIP/ALLEGRO codes, respectively, and Moment Tensor Potential (MTP). For the training and validation sets, I employed the ab-initio molecular dynamics (AIMD) trajectory results and ground state DFT calculations, including the energy, force, and virial database from highly diverse compositions, temperatures, and pressures following a “High Entropy Strategy.” The Deep …

Detection And Diagnosis Of Bacterial Pathogens In Blood And Urine Using Laser-Induced Breakdown Spectroscopy, Emma J.M. Blanchette

Electronic Theses and Dissertations

The aim of this thesis is to expand on and improve the existing techniques used for detecting and identifying bacterial pathogens in clinical specimens with laser-induced breakdown spectroscopy (LIBS). Specifically, the existing experimental procedures, including bacterial sample preparation and data acquisition, as well as the data analysis with chemometric algorithms were investigated. Substantial reductions in LIBS background signal were achieved by implementing rigorous cleaning steps and the introduction of the use of ultrapure water. Following this, a database of LIBS spectra was acquired from specimens of E. coli, S. aureus, E. cloacae, M. smegmatis, and P. …

Machine Learning-Based Jet And Event Classification At The Electron-Ion Collider With Applications To Hadron Structure And Spin Physics, Kyle Lee, James Mulligan, Mateusz Płoskoń, Felix Ringer, Feng Yuan

Physics Faculty Publications

We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC energies, focusing on (i) identifying the flavor of the jet and (ii) identifying the underlying hard process of the event. We propose applications of our machine learning-based jet identification in the key research areas at the future EIC and current Relativistic Heavy Ion Collider program, including enhancing constraints on (transverse momentum dependent) parton distribution functions, improving experimental access to transverse spin asymmetries, studying photon structure, and quantifying the modification of hadrons and jets in …