Computational Physics Research Articles

Toward cost-effective quantum circuit simulation with performance tuning techniques

Quantum circuit simulation is a popular approach to evaluating novel quantum algorithms before a physical quantum computer is available. Unfortunately, the simulation is often done with the full-state quantum circuit simulation scheme, and a huge memory space required by a full-state simulator is a limiting factor for the simulation of a larger qubit system. In this work, in order to support the simulation of a broadened qubit system, storage devices are introduced into the full-state quantum circuit simulation. A vertical qubit simulation design is proposed for the storage-based, full-state quantum circuit simulation, including qubit representation, threading model, and parallel state manipulations over storage devices. An empirical method of simulator parameter tuning is developed to achieve higher simulation performance. Our experimental results show that compared with the state-of-the-art memory-only simulator (QuEST), the storage-based simulation can achieve a 61x higher cost-delay ratio and can simulate a 39-qubit system on a commodity computer. The encouraging results indicate that our proposed simulator can help scale the full-state simulation for larger quantum circuits and achieve higher performance via the performance tuning method.

MOUSE AND KEYBOARD MOTION SENSING USING HAND SIGNS

The project presents a way for Human- Computer Interaction (HCI) that employs a web camera or an integrated camera to control the movement of the indicator instead to the current methods, which typically involve manually entering buttons or altering the positions of a physical mouse. Instead, it can perform the functions that a physical computer mouse does, and it does it through camera and technology for computer vision to manage different mouse events. The Virtual Mouse color recognition application constantly acquires real-time image data and processes it using several filtering and conversion phases. The application utilizes an image processing technique to obtain the coordinates of the desired colors that have emerged within the frames holding a list of color combinations, each of them correlates to a different mouse function. The program will run the mouse function and transform it to an actual mouse function which will be able to applied on the user’s PC if the current color combination passes. The main aim of the project is to virtually interface with the system in virtually every manner imaginable. Keywords: Gesture Recognition, Human Computer Interactions, Graphical User Interface, Open-Source Computer Vision.

Fostering scientific methods in simulations through symbolic regressions

Two-dimensional computer and tablet PC physics simulations have proved to be effective in helping students understand the fundamental principles of physics and related natural processes. However, the current approach to using these simulations tends to follow a repetitive cognitive and procedural pathway, regardless of the specific physical concepts being explored or software environment being used. This approach involves manipulating the simulation interface and collecting data through interaction with controls, widgets, or other contextual elements. Students then attempt to determine how these experimental measurements align with established laws, interactions, or mechanisms, as the teacher might have previously explained. We believe that this approach, while appropriate for education, obscures scientific processes, mainly related to the hypothetico-deductive model. To address this issue, we have developed a simple and adaptable computer environment that makes use of genetic algorithms (GAs) and symbolic regression to derive many of the basic laws of nature from the data collected by students using the popular physics education technology (PhET) simulations environment. Our proposal enables learners to observe how the order and relationships of mathematical tokens are routinely refined as new data points are added to the simulation setting. This iterative distillation technique can also be augmented with the interplay of dimensional analysis. In contrast with other more sophisticated artificial intelligence patterns, GA fit into the realm of grey box machine learning models. These type of evolutionary algorithms achieve the sought results by evolving mathematical models on each stage in an understandable way, which mimics the way scientific breakthroughs are accomplished (over the course of generations of researchers and based of prior knowledge). By implementing this innovative approach, we can provide students with a more authentic empirical experience that fosters a deeper understanding of the principles of science and scientific discovery. Field tests with students supporting this claim have also been carried out.

Numerical Study on Physical Reservoir Computing With Josephson Junctions

Numerical Study on Physical Reservoir Computing With Josephson Junctions

Distributions for Compositionally Differentiating Parametric Discontinuities

Computations in physical simulation, computer graphics, and probabilistic inference often require the differentiation of discontinuous processes due to contact, occlusion, and changes at a point in time. Popular differentiable programming languages, such as PyTorch and JAX, ignore discontinuities during differentiation. This is incorrect for parametric discontinuities —conditionals containing at least one real-valued parameter and at least one variable of integration. We introduce Potto, the first differentiable first-order programming language to soundly differentiate parametric discontinuities. We present a denotational semantics for programs and program derivatives and show the two accord. We describe the implementation of Potto, which enables separate compilation of programs. Our prototype implementation overcomes previous compile-time bottlenecks achieving an 88.1x and 441.2x speed up in compile time and a 2.5x and 7.9x speed up in runtime, respectively, on two increasingly large image stylization benchmarks. We showcase Potto by implementing a prototype differentiable renderer with separately compiled shaders.

Analyzing interviews on computational thinking for introductory physics students: Toward a generalized assessment

Computational thinking in physics has many different forms, definitions, and implementations depending on the level of physics or the institution it is presented in. To better integrate computational thinking in introductory physics, we need to understand what physicists find important about computational thinking in introductory physics. We present a qualitative analysis of 26 interviews asking academic (N_a=18) and industrial (N_i=8) physicists about the teaching and learning of computational thinking in introductory physics courses. These interviews are part of a long-term project toward developing an assessment protocol for computational thinking in introductory physics. We find that academic and industrial physicists value students’ ability to read code and that (or ) and spreadsheets were the preferred computational language or environment used. Additionally, the interviewees mentioned that identifying the core physics concepts within a program, explaining code to others, and good program hygiene (i.e., commenting and using meaningful variable names) are important skills for introductory students to acquire. We also find that while a handful of interviewees note that the experience and skills gained from computation are quite useful for student’s future careers, they also describe multiple limiting factors of teaching computation in introductory physics, such as curricular overhaul, not having “space” for computation’, and student rejection. The interviews show that while adding computational thinking to physics students’ repertoire is important, the importance really comes from using computational thinking to learn and understand physics better. This informs us that the assessment we develop should only include the basics of computational thinking needed to assess introductory physics knowledge. Published by the American Physical Society 2024

Deterministic Lightweight VM placement for HANDLING resource constraint issues in the cloud

The virtual machines (VMs) placement is the subject of current cloud computing research. This study proposes the energy-constrained VM placement technique to address the issue of placing location limits on VMs to satisfy their requirements throughout the VM placement process. Each virtual machine (VM) can only be installed on one of the designated candidate physical machines (PMs) that have sufficient processing power, additionally, in order to satisfy the communication requirements of the associated VMs, there must be sufficient bandwidth between the selected PMs. Choosing where to put a virtual machine (VM) is a crucial task. It involves finding the best physical server or computer to host the VM. Picking the right host is essential for making sure the cloud system uses power wisely, uses resources effectively, and supports good quality of service. This work explore the problem of imposing constraints on the placements of VMs in cloud computing (CC) and offer an alternative perspective on VM placement. This work provides a novel algorithm based on this unique viewpoint to produce the required outcome. To show how successful the proposed Deterministic Lightweight VM placement (DLVMP) is, we run and examine several simulations. The outcomes demonstrate that our technique achieves reduced computing time and improved performance. Simulation results show that the proposed model functions and performs better in terms of blocking probability and computing time than other benchmark algorithms.

Embedding Bifurcations into Pneumatic Artificial Muscle.

Harnessing complex body dynamics has long been a challenge in robotics, particularly when dealing with soft dynamics, which exhibit high complexity in interacting with the environment. Recent studies indicate that these dynamics can be used as a computational resource, exemplified by the McKibben pneumatic artificial muscle, a common soft actuator. This study demonstrates that bifurcations, including periodic and chaotic dynamics, can be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics not present in training data can be embedded through bifurcation embedment, implying the capability to incorporate various qualitatively different patterns into pneumatic artificial muscle without the need to design and learn all required patterns explicitly. Thus, this study introduces a novel approach to simplify robotic devices and control training by reducing reliance on external pattern generators and the amount and types of training data needed forcontrol.

LT협동활동을 기반한 유아 디지털놀이 프로그램이 유아의 놀이성에 미치는 영향

Objectives The purpose of this study is to examine the impact of a digital play program based on LT cooperative learning (Learning Together) on children's playfulness.
 Methods To this end, we conducted a literature review and data collection according to the ADDIE model, and derived content and developed a program to utilize a digital play program based on LT cooperative learning. An experimental study was conducted for 5 weeks from June to July 2022 on 22 children in two classes of 5-year-olds at A Public Kindergarten and B Public Kindergarten located in E City, Gyeonggi-do.
 Results This program attempted to reconstruct the curriculum by linking physical computing educational activities and unplugged activities as digital play for children with the 2019 revised Nuri curriculum, and verified the positive effect of the developed program on children's playfulness.
 Conclusions The results of this study suggest a digital play program based on LT cooperative learning (Learning Together) as a model for integrating curriculum and digital play. In the future, it can be used as a curriculum development material for digital play operation for post-digital generation children in early childhood education fields.

On comparative analysis of graph entropies of symmetrical carbon nanotube Y-junctions

Entropy is crucial in statistical mechanics, thermodynamics, and information theory as it measures a system’s level of randomness or disorder. Entropy is widely used in mathematical chemistry and computational physics to predict the behavior of a system under various conditions. Among numerous carbon nanotube structures, three-terminal carbon nanotube junctions are important structures not only for electrical but also for mechanical appliances. Recently, significant attention has been given to the understanding of carbon nanotube junctions. This research paper focuses on calculating graph entropies based on Zagreb indices for symmetrical single-walled armchair carbon nanotube Y − junctions and comparing the index-entropies of these junctions. The study aims to demonstrate the behavior of the nanotube Y − junctions by adding atoms at the end of the tubes.

Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion

Numerous single-image dehazing algorithms have been developed, employing a spectrum of techniques ranging from intricate physical computations to state-of-the-art deep-learning methodologies. However, conventional deep-learning approaches, particularly those based on standard convolutional neural networks (CNNs), often result in the persistence of residual fog patches when applied to images featuring high fog concentration or heterogeneous fog distribution. In response to this challenge, we propose an innovative solution known as the multi-feature fusion image dehazing network (MFID-Net). This approach employs an end-to-end methodology to directly capture the mapping relationship between hazy and fog-free images. Central to our approach is the introduction of a novel multi-feature fusion (MF) module, strategically designed to address channel and pixel characteristics in regions with uneven or high fog concentrations. Notably, this module achieves effective haze reduction while minimizing computational resources, thereby mitigating the issue of residual fog patches. Experimental results underscore the superior performance of our algorithm compared to similar dehazing methods, as evidenced by higher scores in structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and computational velocity. Moreover, MFID-Net exhibits significant advancements in restoring details within expansive monochromatic areas, such as skies and white walls.

Semi-discrete entropy-preserving surface reconstruction schemes for the shallow water equations: Analysis of physical structures

We aim to introduce a new variant of hydrostatic reconstruction scheme, which is physical-structure-preserving and semi-discrete entropy-preserving for shallow water equations with a discontinuous bottom topography, based on novel surface reconstructions (NSR). The NSR is used to define approximate Riemann states with respect to the water depth and the velocity to compute consistent numerical fluxes across the cell interface and the upwind interface source term. This work is motivated by path-conservative surface reconstructions (Dong (2023) [19]), in which the conditions to satisfy the entropy inequality are complicated. We propose the NSR scheme to satisfy the entropy inequality without the corresponding complicated conditions. A key advantage of the NSR scheme is to preserve the physical structure of the water surface in the sense that when the water surface is continuous, then the defined approximate Riemann state should inherit this property. The physical-structure-preserving property is indispensable for obtaining a more accurate discretized source term and avoiding artificial “waterfall” effects. We prove that the NSR scheme satisfies a semi-discrete entropy condition and can guarantee the water depth to be nonnegative, and maintain the stationary state. We show several computed results to compare the NSR scheme with the hydrostatic reconstruction scheme based on subcell reconstructions and the path-conservative surface reconstruction scheme. We also extend the NSR scheme for pollutant transport in shallow water based on adaptive moving triangular meshes. Finally, several numerical results obtained by the adaptive NSR scheme for pollutant transport in shallow water are shown.

Collaborative Troubleshooting in STEM: A Case Study of High School Students Finding and Fixing Code, Circuit and Craft Challenges in Electronic Textiles

Debugging (or troubleshooting) provides a rich context to foster problem-solving. Yet, while we know much about some problems and strategies that novices face in programming on-screen, we know far less about debugging and troubleshooting in the context of physical computing, where coding issues may overlap with materially embedded problems. In this paper, we study the thought processes novice students employed and the challenges they faced in debugging an electronic textile project with multiple overlapping problems that crossed physical, electronic, and computational domains. We employed a think-aloud protocol to develop an instrumental case study by video-recording 45 minutes of one pair of 9th-grade students debugging and fixing a buggy e-textile project. The problem space included the computational system’s programmatic, electronic, and physical spatial aspects, which are more generally reflective of physical computing systems. We found that (1) students’ troubleshooting was more recursive and less linear than traditional approaches that usually propose linear, procedural, step-wise activities, and (2) students coordinated their approach across multiple modalities, taking advantage of a distributed set of tools and people in order to tackle a complex set of problems. In the discussion, we address various pedagogical implications for improving teaching about troubleshooting.

Webcam-based gaze estimation for computer screen interaction.

This paper presents a novel webcam-based approach for gaze estimation on computer screens. Utilizing appearance based gaze estimation models, the system provides a method for mapping the gaze vector from the user's perspective onto the computer screen. Notably, it determines the user's 3D position in front of the screen, using only a 2D webcam without the need for additional markers or equipment. The study presents a comprehensive comparative analysis, assessing the performance of the proposed method against established eye tracking solutions. This includes a direct comparison with the purpose-built Tobii Eye Tracker 5, a high-end hardware solution, and the webcam-based GazeRecorder software. In experiments replicating head movements, especially those imitating yaw rotations, the study brings to light the inherent difficulties associated with tracking such motions using 2D webcams. This research introduces a solution by integrating Structure from Motion (SfM) into the Convolutional Neural Network (CNN) model. The study's accomplishments include showcasing the potential for accurate screen gaze tracking with a simple webcam, presenting a novel approach for physical distance computation, and proposing compensation for head movements, laying the groundwork for advancements in real-world gaze estimation scenarios.

Preface

The Center for Space Plasma and Aeronomic Research (CSPAR) at the University of Alabama in Huntsville (UAH) and Maison de la Simulation, a joint laboratory at the French Alternative Energies and Atomic Energy Commission (CEA), National Center for Scientific Research (CNRS), University of Paris-Saclay, and University of Versailles-St Quentin, organized the 15th International Conference on Numerical Modeling of Space Plasma Flows (ASTRONUM-2023) on June 26—30, 2023 in Pasadena, California, USA.The Program Committee consisted of Nikolai Pogorelov (University of Alabama in Huntsville/CSPAR, USA, chair), Edouard Audit (CEA Maison de la Simulation, Gif-sur-Yvette, France, co-chair), Wes Bethel (San Francisco State University, USA), Amitava Bhattacharjee (Princeton Plasma Physics Laboratory, USA), Phillip Colella (Lawrence Berkeley National Laboratory, USA), Tomoyuki Hanawa (Chiba University, Japan), Maria Elena Innocenti (Ruhr University Bochum, Germany), Kanya Kusano (Nagoya University, Japan), Dongwook Lee (University of California, Santa Cruz), Jon Linker (Predictive Science Inc., USA), Anthony Mezzacappa (University of Tennessee, Knoxville, USA), Ewald Müller (Max-Planck-Institute for Astrophysics, Garching, Germany), Dongsu Ryu (Ulsan National Institute of Science and Technology, Korea), James Stone (Princeton University), and Gary P. Zank (University of Alabama in Huntsville, USA).The conference attracted over 90 scientists from 15 countries representing different branches of the plasma simulation community. The distinctive feature of this conference is a combination of diverse research topics, all of which are essential for performing high-resolution, continuum mechanics and particle, simulations of physical phenomena in space physics and astrophysics. Among such topics were software packages for modeling and analyzing plasma flows; advanced numerical methods for space and astrophysical flows; large-scale fluid-based, kinetic, and hybrid simulations; turbulence and cosmic ray transport; and magnetohydrodynamics. The discussed applications included cosmology and galaxy formation, supernova explosions, physics of the Sun-heliosphere-magnetosphere environments, space weather, the interstellar medium and star formation, stellar physics, experimental plasma physics, astrophysical accretion, numerical methods for ideal and non-ideal, relativistic and nonrelativistic MHD, etc. The proceedings volume is structured so that it covers all of these topics.The structure of the ASTRONUM conference series is based on the idea that modelers working in seemingly distant fields should have an opportunity to share their scientific achievements with the broad community of computational scientists performing numerical experiments. As in previous ASTRONUM meetings, we were interested in physical systems that are coupled across a multiplicity of spatial and temporal that incorporate diverse physical processes.The contributors to this volume are both young researchers and renowned experts in space physics and astrophysics, applied mathematics, and computational physics. This volume describes the application of numerical methods and the algorithms themselves, allowing us to discuss the challenges that theory imposes on numerical schemes for solving partial differential equations describing collisional and collisionless processes in space and astrophysical plasmas.We would like to thank the participants who submitted their papers to Proceedings of ASTRONUM-2023 and especially to those who reviewed manuscripts thus ensuring the high quality of this publication. We also are grateful to Adele Corona (ICNSMeetings.com) for the excellent management of the conference.This publication will be useful to graduate and postgraduate students majoring in space physics, astrophysics, numerical, engineering, and applied mathematics. It is also aimed at specialists in applied mathematics and various fields of physics that involve flows of partially ionized plasmas at both the collisional and collisionless levels.Nikolai V. Pogorelov, Edouard Audit, and Gary P. ZankMarch 3, 2024

541 A Framework for Multicultural and Multidisciplinary Near-Peer Mentoring for Artificial Intelligence in Healthcare Education: A University of Florida Friend Group

OBJECTIVES/GOALS: This work aims to explore how citizen science serves as a transformative frame work to bridge scientific knowledge, focusing on its potential to enhance transdisciplinary learning in artificial intelligence (AI) biomedical and clinical sciences by facilitating near-peer mentoring. METHODS/STUDY POPULATION: Our group of eight friends comprise a multicultural and multidisciplinary cohort including students from the USA, Philippines, Indonesia, and Guatemala pursuing PhD degrees in electrical and computer engineering, epidemiology, physics, and MD, PharmD, and DMD degrees. We engage in shared online courses, collaborative projects, and abstract submissions. Employing our collective knowledge, we design interactive learning experiences, support each other’s initiatives, and collaboratively develop lectures and presentations. We in tend to expand collaborations in biomedical AI education while fostering principles of experiential and collaborativelearning, constructivism, and authentic inquiry. RESULTS/ANTICIPATED RESULTS: Our recent successes include submittedconference abstracts on data science and AI education in pharmacy and the facilitation of a guest lecture in health informatics. Additionally, we are currently collaborating on seven biomedical machine learning projects in radio frequency engineering, aiming for conference submissions. Moving forward, our goal is to expand our group, support the formation of similar communities, and promote data science and AI literacy in biomedical and clinical contexts. We aspire to extend this knowledge to families, classmates, and eventually patients, facilitating a broader understanding of the role of AI in healthcare. DISCUSSION/SIGNIFICANCE: We believe diverse expertise and pedagogical theories can help demonstrate the potential of citizen science to democratize scientific experience. By nurturing collaborative networks our efforts aim to bridge gaps between disciplines and enhance the broader public’s understanding of AI in healthcare.

The influence of hyperchaoticity, synchronization, and Shannon entropy on the performance of a physical reservoir computer.

In this paper, we analyze the dynamic effect of a reservoir computer (RC) on its performance. Modified Kuramoto's coupled oscillators are used to model the RC, and synchronization, Lyapunov spectrum (and dimension), Shannon entropy, and the upper bound of the Kolmogorov-Sinai entropy are employed to characterize the dynamics of the RC. The performance of the RC is analyzed by reproducing the distribution of random, Gaussian, and quantum jumps series (shelved states) since a replica of the time evolution of a completely random series is not possible to generate. We demonstrate that hyperchaotic motion, moderate Shannon entropy, and a higher degree of synchronization of Kuramoto's oscillators lead to the best performance of the RC. Therefore, an appropriate balance of irregularity and order in the oscillator's dynamics leads to better performances.

Analysis of the Application for Random Walk in Computer Science, Physics and Mathematical Equations

Analysis of the Application for Random Walk in Computer Science, Physics and Mathematical Equations

DETERMINATION OF THE PERFORMANCE OF LIGHT SHELVES FOR MORE EFFECTIVE BENEFIT FROM DAYLIGHT IN BUILDINGS

Studies on the efficient use of daylight in sustainable architectural design and energy conservation are increasing. Indoor lighting methods include daylighting, artificial, and integrated lighting. Basic priorities in lighting are such as “effective use of daylight”, “uniform illumination in space”, “glare control”, “visual connection with the external environment” and “daylight harvesting.” Advanced contemporary systems include light shelves and light tubes. Light shelves consist of horizontal or slightly angled elements that can be applied to the inner and outer surfaces of the window openings, usually at eye level, to block the daylight or to reflect it to the ceiling, integrated with the facade, or added later. In this research, light shelves, which is one of the advanced daylighting methods, are emphasized. Daylight analysis method was carried out with the help of a physical model and computer simulation techniques using DiaLux software. To effectively utilize the daylight factor in the internal volume through light shelf, certain parameters such as the height and angle of the light shelf, date and time, and direction of the room's opening, play a crucial role. In daylight analyzes, these parameters were subjected to experimental testing both in physical models and through computer simulations. As a result of the comparison of the obtained data, alternatives that will provide the opportunity to benefit from daylight in the most effective way have been identified. The results obtained have unique value and widespread impact in terms of sustainable architecture and energy saving. The study's originality lies in its specific measurements of the latitude in which it is located, as it is the first time the study is conducted under Eskisehir's conditions. In addition, the examination of the advantages and disadvantages of the light shelf in specific combinations is another original side of the project.

Special Topic: Frontiers and Prospects of Computational Physics in National Natural Science Foundation of China

Special Topic: Frontiers and Prospects of Computational Physics in National Natural Science Foundation of China