Bulletin of the American Physical Society
2020 Annual Meeting of the APS Four Corners Section (Virtual)
Volume 65, Number 16
Friday–Saturday, October 23–24, 2020; Albuquerque, NM (Virtual)
Session L01: Computational Physics ILive
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Chair: William Fairbank, Jr., Colorado State University |
Saturday, October 24, 2020 11:00AM - 11:12AM Live |
L01.00001: Clustering geospatial data for machine learning modeling of ambient soundscapes Mitchell Cutler, Katrina Pedersen, Mark Transtrum, Kent Gee, Shane Lympany, Michael James Outdoor ambient acoustical environments may be predicted through supervised machine learning using geospatial features as inputs. Previous work used K-Means clustering applied to the geospatial features to identify distinct geographic regions. The clustering results help provide physical insights regarding which features are likely to play the largest roles in supervised learning models and which locations are impacted by different acoustic training data. However, these results may be sensitive to details of the geospatial data, such as how the data are scaled or the presence of similar redundant features. This work builds on previous results by constructing a reduced feature set by removing redundant geospatial features and by using a physically motivated scaling scheme. Clustering analysis applied to this dataset indicates that the contiguous United States can be naturally clustered into eight human-interpretable geographic regions. Hierarchical clustering is used to further subdivide these eight clusters into more fine-grained regions. One finding of interest is that no geospatial layer in the present soundscape model uniquely identifies rivers. These results will guide further geospatial layer development and acoustical data collection for more accurate soundscape models. [Preview Abstract] |
Saturday, October 24, 2020 11:12AM - 11:24AM Live |
L01.00002: Methods in Computational Enzyme Design Austin Jarrett Enzyme engineering is a powerful field that makes use of physical, chemical, biological, and computational principles to develop novel catalysts for desired chemical products. Traditional methods of enzyme engineering require many rounds of mutation and screening for desired properties. In recent years, computational enzyme design has emerged as an attractive alternative to traditional techniques to reduce time and cost with increased ability to explore the vast sequence space. A wide variety of computational tools and methods have been developed in to aid in computational enzyme design. Over 80 published studies in the field of enzyme engineering from the past five years have been studied and categorized to evaluate differences in computational approaches and strengths of each one. This search has provided valuable insight regarding areas requiring improvement in this field and possible future applications of these tools. [Preview Abstract] |
Saturday, October 24, 2020 11:24AM - 11:36AM Live |
L01.00003: Mathematical Models for Living Forms in Medical Physics Submodel 1: The Information Processing from Teeth to Nerves Christina Pospisil This talk continues the presentation at APS March Meeting 2019 and APS April Meeting 2019. In this part of the project the first submodel is presented; The information processing from teeth to the nerves. Information processing is modeled via p-waves passing through the tooth layers enamel and dentin. Odontoblasts located in the liquid in the tubules of the tooth dentin layer perform finally the transformation into electrical information (an electrical signal) that passes along nerves. The presentation was scheduled for the APS March Meeting 2020 Conference (the APS March Meeting 2020 Conference got canceled because of Covid-19), the presentation was given at the APS April Meeting 2020 Conference. [Preview Abstract] |
Saturday, October 24, 2020 11:36AM - 11:48AM Live |
L01.00004: MPI-parallel Molecular Dynamics Trajectory Analysis with the H5MD Format in MDAnalysis Edis Jakupovic, Oliver Beckstein With the growing size of molecular dynamics trajectory files, file I/O has become a bottleneck in the analysis of MD trajectories. If one could open and analyze a trajectory file in parallel, analysis speeds could go from hours to minutes. Previous work [1] found that parallel I/O via MPI-IO and HDF5 led to near ideal strong scaling. However, the previous feasibility study did not provide a usable implementation of a true MD trajectory format. The goal of this work was to add a parallel HDF5 file format coordinate reader to MDAnalysis, a widely used Python library for the analysis of MD simulation data. We added a trajectory reader and performed benchmarks on two typical workloads with different performance characteristics: An I/O bound task and a compute-bound task. These benchmarks were performed on a typical desktop resource and on ASU's Agave supercomputer with the BeeGFS parallel file system, and both showed substantial speedups with our parallel reader. The addition of the HDF5 reader provides a foundation for the development of parallel trajectory analysis with MPI and the MDAnalysis package. [1] M. Khoshlessan, I. Paraskevakos, G. C. Fox, S. Jha, and O. Beckstein. In Conc. {\&} Comp.: Prac. {\&} Exp., 2020. doi: 10.1002/cpe.5789 [Preview Abstract] |
Saturday, October 24, 2020 11:48AM - 12:00PM Live |
L01.00005: Convolutional neural network-based modeling of an ultrafast laser Aasma Aslam, sandra Gail Biedron, Yong Ma, Murphy Murphy, Milos Burger, Manel Martinez-Ramon, John Nees, Salvador Sosa, Alec Thomas, Karl Krushelnick We continue to see massive developments in the synergistic fields of particle accelerators and lasers. These beam sources have proven over and over to be essential tools for scientists, engineers, and technologists in many fields, including discovery science, medicine, environmental applications, energy application, and industry. Machine learning (ML) techniques to help the model and control of the laser beam for driving particle accelerators. We know from our experiences and the experiences of others that using advanced control techniques, including ML can often improve processes and machine performance. Our research is based on Convolutional-Neural-Network; as one of the classes of neural-networks. In this research, we report the Neural Network (NN) based on the feedforward-backpropagation control system to model the relationship between the DAZZLER inputs and the temporal pulse width of the femtosecond pulsed laser system. [Preview Abstract] |
Saturday, October 24, 2020 12:00PM - 12:12PM |
L01.00006: The Development of an Automatic Rainwater Harvesting and Water Supply System Using Solar Panels Ji Won Kim Automatic rainwater harvesting and water supply system, using photovoltaic cell modules, can be used as an effective auxiliary device for farming water supply. This study aims to identify ways to utilize photovoltaic power generation facility as a means to collect rainwater during the rainy season so as to use it during the dry season. This new method can contribute to the development of automatic farming and gardening system by increasing the efficiency of the photovoltaic power generation mechanism. Arduino was used to make a solar tracker, maximize the efficiency of photovoltaic power generation, detect rainwater, and make the automatic transition to horizontal form. For the efficient management of collected water, a sensor was used to check soil water conditions based on soil water content (50~80%). The system was made to supply water automatically. A conducted experiment to generate voltage according to the changing temperature of photovoltaic cell modules showed an increase in temperature, which then led to a decrease in generated voltage. [Preview Abstract] |
Saturday, October 24, 2020 12:12PM - 12:24PM |
L01.00007: Study on the Nonlinear Dynamic Characteristics of Structures under Seismic Loadings Jiwon Moon, Richard Kyung In the presented dynamic analysis, the governing equations of motion are derived using two degrees of freedom in the system, taking into account a variety of conditions. The predictive dynamics model for impact-absorption systems is used in order to simulate the vibrational movement of the multi-link manipulator such as from buildings to car suspension systems. Recent advances in the predictive dynamics allow the user not only to predict dynamics-based mechanics, but also to perform the virtual simulations of the system. In this paper, the dynamic analysis in impact-absorption systems was performed by deriving the governing differential equation of motion and plotting the amplitude and the phase of the masses with respect to the frequency. Periodic excitation such as trigonometric function and forced vibration were applied to the spring mass system to find the motion of the seismic instrument or vibrometer. Instead of using soft springs, very hard springs were tested to give a high natural frequency of the system. A system, whose damping is negligible, was also employed to find the magnitude of the vibration. The study shows the addition of vibration absorber devices resulted in a significant improvement in the system’s stability. [Preview Abstract] |
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