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Full-Text Articles in Physics
Lecture 12: Recent Advances In Time Integration Methods And How They Can Enable Exascale Simulations, Carol S. Woodward
Lecture 12: Recent Advances In Time Integration Methods And How They Can Enable Exascale Simulations, Carol S. Woodward
Mathematical Sciences Spring Lecture Series
To prepare for exascale systems, scientific simulations are growing in physical realism and thus complexity. This increase often results in additional and changing time scales. Time integration methods are critical to efficient solution of these multiphysics systems. Yet, many large-scale applications have not fully embraced modern time integration methods nor efficient software implementations. Hence, achieving temporal accuracy with new and complex simulations has proved challenging. We will overview recent advances in time integration methods, including additive IMEX methods, multirate methods, and parallel-in-time approaches, expected to help realize the potential of exascale systems on multiphysics simulations. Efficient execution of these methods …
Qwasi: The Quantum Walk Simulator, Warren V. Wilson
Qwasi: The Quantum Walk Simulator, Warren V. Wilson
Theses and Dissertations
As quantum computing continues to evolve, the ability to design and analyze novel quantum algorithms becomes a necessary focus for research. In many instances, the virtues of quantum algorithms only become evident when compared to their classical counterparts, so a study of the former often begins with a consideration of the latter. This is very much the case with quantum walk algorithms, as the success of random walks and their many, varied applications have inspired much interest in quantum correlates. Unfortunately, finding purely algebraic solutions for quantum walks is an elusive endeavor. At best, and when solvable, they require simple …
Exploring Mathematical Strategies For Finding Hidden Features In Multi-Dimensional Big Datasets, Tri Duong, Fang Ren, Apurva Mehta
Exploring Mathematical Strategies For Finding Hidden Features In Multi-Dimensional Big Datasets, Tri Duong, Fang Ren, Apurva Mehta
STAR Program Research Presentations
With advances in technology in brighter sources and larger and faster detectors, the amount of data generated at national user facilities such as SLAC is increasing exponentially. Humans have a superb ability to recognize patterns in complex and noisy data and therefore, data is still curated and analyzed by humans. However, a human brain is unable to keep up with the accelerated pace of data generation, and as a consequence, the rate of new discoveries hasn't kept pace with the rate of data creation. Therefore, new procedures to quickly assess and analyze the data are needed. Machine learning approaches are …
Generating Random Walks And Polygons With Thickness In Confinement, Sai Sindhuja Veeramachaneni
Generating Random Walks And Polygons With Thickness In Confinement, Sai Sindhuja Veeramachaneni
Masters Theses & Specialist Projects
Algorithms to generate walks (chains of unit-length, freely-jointed segments) and polygons (closed walks) in spherical confinements have been developed in the last few years. These algorithms generate polygons inside spherical confinement based on their mathematically derived probability distributions. The generated polygons do not occupy any volume { although that would be useful for some applications. This thesis investigates how to generate walks and polygons which occupy some volume in spherical confinement. More specifically, in this thesis, existing methods described in the literature have been studied and implemented to generate walks and polygons in confinement. Additionally, these methods were adapted to …
Autonomous Entropy-Based Intelligent Experimental Design, Nabin Kumar Malakar
Autonomous Entropy-Based Intelligent Experimental Design, Nabin Kumar Malakar
Legacy Theses & Dissertations (2009 - 2024)
The aim of this thesis is to explore the application of probability and information theory in experimental design, and to do so in a way that combines what we know about inference and inquiry in a comprehensive and consistent manner.
A Neural Network: Family Competition Genetic Algorithm And Its Application In Electromagnetic Optimization, Chien Hsun Chen, P. Y. Chen, H. Weng
A Neural Network: Family Competition Genetic Algorithm And Its Application In Electromagnetic Optimization, Chien Hsun Chen, P. Y. Chen, H. Weng
Chien Hsun Chen
This study proposes a neural network-family competition genetic algorithm (NN-FCGA) for solving the electromagnetic (EM) optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN) and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA) to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are …
A Fast Serial Algorithm For The Finite Temperature Quenched Potts Model, Gregory N. Hassold, Elizabeth A. Holm
A Fast Serial Algorithm For The Finite Temperature Quenched Potts Model, Gregory N. Hassold, Elizabeth A. Holm
Physics Publications
An efficient serial algorithm for finite temperature, quenched Potts model simulations of domain evolution has been developed. This ''n‐fold way'' algorithm eliminates unsuccessful spin flip attempts a priori by flipping sites with a frequency proportional to their site activity, defined as the sum of the probability of success for every possible spin flip at that site. Finite temperature efficiency for high‐spin degeneracy systems is achieved by utilizing a new, analytical expression for the portion of the site activity due to flips to non-neighbor spin values. Hence, to determine the activity of a site, only flips to the …