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2nd International Annual Meeting on STEM Education, IAMSTEM 2019

作者： Gamale, Janne Noelle L. Acut, Dharel P. Niere, Karla Marie Frances P. Silagan, Gwen Stephany S. Curaraton, Ethel P. Latonio, Guadalupe C. Latonio, Rhett Anthony C. Magsayo, Joy R. High School Department Sotero B. Cabahug Forum for Literacy Consolacion Cebu6001 Philippines Applied Physics Program National Institute of Physics College of Science University of the Philippines - Diliman Roxas Ave. Diliman Quezon City1101 Philippines Science Education Program Graduate School College of Teacher Education Cebu Normal University Osmeña Blvd. Cebu City Cebu6000 Philippines Department of Science and Mathematics Education College of Education Mindanao State University - Iligan Institute of Technology Iligan City9200 Philippines

A hurdle to effective science education in schools is often the lack of teaching materials. Teaching science education in the 21st century requires direct visualization and manipulation with effective use and implementation for meaningful learning to occur, thus, unleashing students' potential towards the lesson, boosting their engagement in learning, and applying the knowledge into skills. This study was carried out to develop a do-it-yourself gel electrophoresis apparatus. This study was conducted in Cebu Normal University, Philippines with nineteen (19) teachers from the Methods of Teaching Science and Instructional Trends and Issues Class who evaluated the apparatus in terms of usability and functionality. The respondents tested the Gel Electrophoresis apparatus by conducting the experiment and they evaluated the apparatus' functionality and usability by using an evaluation sheet which is composed of eleven (11) usability questions, four (4) functionality questions. Suggestions were also asked to improve the said apparatus. After gathering data, the suggestions made by the respondents were incorporated which resulted to the improved D.I.Y. Gel Electrophoresis Apparatus. A manual with the procedures was also designed to further guide students and teachers on how to build the apparatus. With the findings and results obtained in the study, the respondents recommended the usage of the developed do-it-yourself gel electrophoresis apparatus in their respective classes and agreed that it is a good source of learning the concept of gel electrophoresis as a 21st century biological technique. The apparatus was easy to set-up and is of low-cost. During experimentation, the apparatus yielded with a positive result - the separation of food dyes which follows the principle of gel electrophoresis. Moreover, the experiment encourages creativity and it helps with the understanding of the nature of DNA and the concept of gel electrophoresis as a 21st century biological skills. ©

关键词： Students

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Global well-posedness and regularity of 3D Burgers equation with multiplicative noise

arXiv

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arXiv 2021年

作者： Dong, Zhao Guo, Boling Wu, Jiang-Lun Zhou, Guoli RCSDS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Institute of Applied Physics and Computational Mathematics P.O. Box 8009 Beijing100088 China Department of Mathematics Computational Foundry Swansea University SwanseaSA1 8EN United Kingdom School of Statistics and Mathematics Chongqing University Chongqing400044 China

In this paper, we develop low regularity theory for 3D Burgers equation perturbed by a linear multiplicative stochastic force. This method is new and essentially different from the deterministic partial differential equations(PDEs). Our results and method can be widely applied to other stochastic hydrodynamic equations and the deterministic PDEs. As a further study, we establish a random version of maximum principle for random 3D Burgers equations, which will be an important tool for the study of 3D stochastic Burgers equations. As we know establishing moment estimates for highly nonlinear stochastic hydrodynamic equations is difficult. But moment estimates are very important for us to study the probabilistic properties and long-time behavior for the stochastic systems. Here, the random maximum principle helps us to achieve some important moment estimates for 3D stochastic Burgers equations and lays a solid foundation for the further study of 3D stochastic Burgers equations. © 2021, CC BY.

关键词： Maximum principle

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physics INFORMED MACHINE LEARNING WITH SMOOTHED PARTICLE HYDRODYNAMICS: HIERARCHY OF REDUCED LAGRANGIAN MODELS OF TURBULENCE

arXiv

引用

arXiv 2021年

作者： Woodward, Michael Tian, Yifeng Hyett, Criston Fryer, Chris Stepanov, Mikhail Livescu, Daniel Chertkov, Michael Graduate Interdisciplinary Program in Applied Mathematics UArizona TucsonAZ85721 United States Department of Mathematics UArizona TucsonAZ85721 United States Computer Computational and Statistical Sciences Division LANL Los AlamosNM87544 United States

Building efficient, accurate and generalizable reduced order models of developed turbulence remains a major challenge. This manuscript approaches this problem by developing a hierarchy of parameterized reduced Lagrangian models for turbulent flows, and investigates the effects of enforcing physical structure through Smoothed Particle Hydrodynamics (SPH) versus relying on neural networks (NN)s as universal function approximators. Starting from Neural Network (NN) parameterizations of a Lagrangian acceleration operator, this hierarchy of models gradually incorporates a weakly compressible and parameterized SPH framework, which enforces physical symmetries, such as Galilean, rotational and translational invariances. Within this hierarchy, two new parameterized smoothing kernels are developed in order to increase the flexibility of the learn-able SPH simulators. For each model we experiment with different loss functions which are minimized using gradient based optimization, where efficient computations of gradients are obtained by using Automatic Differentiation (AD) and Sensitivity Analysis (SA). Each model within the hierarchy is trained on two data sets associated with weekly compressible Homogeneous Isotropic Turbulence (HIT): (1) a validation set using weakly compressible SPH;and (2) a high fidelity set from Direct Numerical Simulations (DNS). Numerical evidence shows that encoding more SPH structure improves generalizability to different turbulent Mach numbers and time shifts, and that including the novel parameterized smoothing kernels improves the accuracy of SPH at the resolved scales. Copyright © 2021, The Authors. All rights reserved.

关键词： Parameterization

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Discrete Boltzmann multi-scale modeling of non-equilibrium multiphase flows

arXiv

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arXiv 2022年

作者： Gan, Yanbiao Xu, Aiguo Lai, Huilin Li, Wei Sun, Guanglan Succi, Sauro Hebei Key Laboratory of Trans-Media Aerial Underwater Vehicle School of Liberal Arts and Sciences North China Institute of Aerospace Engineering Langfang065000 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics P. O. Box 8009-26 Beijing100088 China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing100081 China HEDPS Center for Applied Physics and Technology College of Engineering Peking University Beijing100871 China Fujian Normal University Fuzhou350117 China School of Physics Beijing Institute of Technology Beijing100081 China Center for Life Nano Science at La Sapienza Fondazione Istituto Italiano di Tecnologia Viale Regina Margherita 295 Roma00161 Italy Physics Department Institute for Applied Computational Science John A. Paulson School of Applied Science and Engineering Harvard University Oxford Street 29 CambridgeMA02138 United States

The aim of this paper is twofold: the first is to formulate and validate a multi-scale discrete Boltzmann method (DBM) based on density functional kinetic theory for thermal multiphase flow systems, ranging from continuum to transition flow regime;the second is to present some new insights into the thermo-hydrodynamic non-equilibrium (THNE) effects in the phase separation process. Methodologically, DBM includes three main pillars: (i) the determination of the fewest kinetic moment relations, which are required by the description of significant THNE effects beyond the realm of continuum fluid mechanics, (ii) the construction of appropriate discrete equilibrium distribution function recovering all the desired kinetic moments, (iii) the detection, description, presentation and analysis of THNE based on the moments of the non-equilibrium distribution (f − f(eq)). The incorporation of appropriate additional higher-order thermodynamic kinetic moments considerably extends the DBM’s capability of handling larger values of the liquid-vapor density ratio, curbing spurious currents, and ensuring mass-momentum-energy conservation. Compared with the DBM with only first-order THNE (Gan et al. 2015), the model retrieves kinetic moments beyond the third-order super-Burnett level, and is accurate for weak, moderate, and strong THNE cases even when the local Knudsen number exceeds 1/3. Physically, the ending point of the linear relation between THNE and the concerned physical parameter provides a distinct criterion to identify whether the system is near or far from equilibrium. Besides, the surface tension refrains the local THNE around the interface, but expands the THNE range and strengthens the THNE intensity away from the interface through interface smoothing and widening. Copyright © 2022, The Authors. All rights reserved.

关键词： Kinetic theory

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Extended Fayans energy density functional: optimization and analysis

arXiv

引用

arXiv 2024年

作者： Reinhard, Paul-Gerhard O'Neal, Jared Wild, Stefan M. Nazarewicz, Witold Institut für Theoretische Physik II Universität Erlangen-Nürnberg ErlangenD-91058 Germany Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Industrial Engineering and Management Sciences Northwestern University EvanstonIL60208 United States Facility for Rare Isotope Beams Michigan State University East LansingMI48824 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States

The Fayans energy density functional (EDF) has been very successful in describing global nuclear properties (binding energies, charge radii, and especially differences of radii) within nuclear density functional theory. In a recent study, supervised machine learning methods were used to calibrate the Fayans EDF. Building on this experience, in this work we explore the effect of adding isovector pairing terms, which are responsible for different proton and neutron pairing fields, by comparing a 13D model without the isovector pairing term against the extended 14D model. At the heart of the calibration is a carefully selected heterogeneous dataset of experimental observables representing ground-state properties of spherical even-even nuclei. To quantify the impact of the calibration dataset on model parameters and the importance of the new terms, we carry out advanced sensitivity and correlation analysis on both models. The extension to 14D improves the overall quality of the model by about 30%. The enhanced degrees of freedom of the 14D model reduce correlations between model parameters and enhance sensitivity. Copyright © 2024, The Authors. All rights reserved.

关键词： Density functional theory

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Nuclear Neural Networks: Emulating Late Burning Stages in Core Collapse Supernova Progenitors

arXiv

引用

arXiv 2025年

作者： Grichener, Aldana Renzo, Mathieu Kerzendorf, Wolfgang E. Farmer, Rob de Mink, Selma E. Bellinger, Earl Patrick Chan, Chi-Kwan Chen, Nutan Farag, Ebraheem Justham, Stephen Steward Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Max Planck Institute for Astrophysics Karl-Schwarzschild-Str. 1 Garching85748 Germany Department of Physics Technion Haifa3200003 Israel Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Ludwig-Maximilians-Universitat Munchen Geschwister-Scholl-Platz 1 Munchen80539 Germany Department of Astronomy Yale University New HavenCT06511 United States Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Avenue TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 North Santa Rita TucsonAZ85721 United States Machine Learning Research Lab Volkswagen AG Munich38440 Germany

One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully-coupled stiff ordinary differential equations (ODEs), making the simulations computationally intensive and prone to numerical instability. To overcome this barrier, we design a nuclear neural network (NNN) framework with multiple hidden layers to emulate nucleosynthesis calculations and conduct a proof-of-concept to evaluate its performance. The NNN takes the temperature, density and composition of a burning region as input and predicts the resulting isotopic abundances along with the energy generation and loss rates. We generate training sets for initial conditions corresponding to oxygen core depletion and beyond using large nuclear reaction networks, and compare the predictions of the NNNs to results from a commonly used small net. We find that the NNNs improve the accuracy of the electron fraction by 280 − 660 % and the nuclear energy generation by 250 − 750 %, consistently outperforming the small network across all timesteps. They also achieve significantly better predictions of neutrino losses on relatively short timescales, with improvements ranging from 100 − 106 %. While further work is needed to enhance their accuracy and applicability to different stellar conditions, integrating NNN trained models into stellar evolution codes is promising for facilitating large-scale generation of core-collapse supernova (CCSN) progenitors with higher physical fidelity. © 2025, CC BY.

关键词： Nucleosynthesis

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Stochastic Paleoclimatology: Modeling the EPICA Ice Core Climate Records

arXiv

引用

arXiv 2022年

作者： Keyes, N.D.B. Giorgini, L.T. Wettlaufer, J.S. Program in Applied Mathematics Yale University New HavenCT06520 United States Department of Earth & Planetary Sciences Yale University New HavenCT06520 United States Nordic Institute for Theoretical Physics Royal Institute of Technology Stockholm University Stockholm10691 Sweden Department of Physics Yale University New HavenCT06520 United States

We analyze and model the stochastic behavior of paleoclimate time series and assess the implications for the coupling of climate variables during the Pleistocene glacial cycles. We examine 800 kiloyears of carbon dioxide, methane, nitrous oxide and temperature proxy data from the EPICA Dome-C ice core, which are characterized by 100 ky glacial cycles overlain by fluctuations across a wide range of time scales. We quantify this behavior through multifractal time-weighted detrended fluctuation analysis, which distinguishes near-red-noise and white-noise behavior below and above the 100 ky glacial cycle respectively in all records. This allows us to model each time series as a one-dimensional periodic nonautonomous stochastic dynamical system, and assess the stability of physical processes and the fidelity of model-simulated time series. We extend this approach to a four-variable model with intervariable coupling terms, which we interpret in terms of possible interrelationships among the four time series. Within the framework of our coupling coefficients, we find that carbon dioxide and temperature act to stabilize each other and methane and nitrous oxide, whereas the latter two destabilize each other and carbon dioxide and temperature. We also compute the response function for each pair of variables to assess the model performance by comparison to the data and confirm the model predictions regarding stability amongst variables. Taken together, our results are consistent with glacial pacing dominated by carbon dioxide and temperature that is modulated by terrestrial biosphere feedbacks associated with methane and nitrous oxide emissions. © 2022, CC BY.

关键词： Carbon dioxide

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Paths in a Network of Polydisperse Spherical Droplets

Paths in a Network of Polydisperse Spherical Droplets

引用

2022 Conference on Artificial Life, ALIFE 2022

作者： Schneider, Johannes Josef Faggian, Alessia Holler, Silvia Casiraghi, Federica Li, Jin Sanahuja, Lorena Cebolla Matuttis, Hans-Georg Hanczyc, Martin Michael Barrow, David Anthony Weyland, Mathias Sebastian Flumini, Dandolo Hotz, Peter Eggenberger Eschle, Patrik Diaz, Aitor Patiño Füchslin, Rudolf Marcel Institute of Applied Mathematics and Physics School of Engineering Zurich University of Applied Sciences Technikumstr. 9 Winterthur8401 Switzerland School of Engineering Cardiff University Queen’s Buildings 14-17 The Parade CardiffCF24 3AA United Kingdom Laboratory for Artificial Biology Department of Cellular Computational and Integrative Biology University of Trento Via Sommarive 9 Povo38123 Italy Department of Mechanical Engineering and Intelligent Systems The University of Electrocommunications Chofu Chofugaoka 1-5-1 Tokyo182-8585 Japan Chemical and Biological Engineering University of New Mexico MSC01 1120 AlbuquerqueNM87131-0001 United States European Centre for Living Technology S.Marco 2940 Venice30124 Italy

We simulate the movement and agglomeration of oil droplets in water under constraints, like confinement, using a simplified stochastic-hydrodynamic model. In the analysis of the network created by the droplets in the agglomeration, we focus on the paths between pairs of droplets and compare the computational results for various system sizes. © Artificial Life Conference Proceedings 2022.

关键词： Agglomeration

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On the importance of assessing topological convergence in Bayesian phylogenetic inference

arXiv

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arXiv 2024年

作者： Brusselmans, Marius Carvalho, Luiz Max Hong, Samuel L. Gao, Jiansi Matsen, Frederick A. Rambaut, Andrew Lemey, Philippe Suchard, Marc A. Dudas, Gytis Baele, Guy Department of Microbiology Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium School of Applied Mathematics Getulio Vargas Foundation Praia de Botafogo 190 Rio de Janeiro22250-900 Brazil Computational Biology Program Fred Hutchinson Cancer Center SeattleWA98109 United States Howard Hughes Medical Institute Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Department of Statistics University of Washington SeattleWA United States Institute of Ecology and Evolution University of Edinburgh EdinburghEH9 3FL United Kingdom Department of Biostatistics Fielding School of Public Health University of California Los AngelesCA90095 United States Institute of Biotechnology Life Sciences Center Vilnius University Vilnius Lithuania

Modern phylogenetics research is often performed within a Bayesian framework, using sampling algorithms such as Markov chain Monte Carlo (MCMC) to approximate the posterior distribution. These algorithms require careful evaluation of the quality of the generated samples. Within the field of phylogenetics, one frequently adopted diagnostic approach is to evaluate the effective sample size (ESS) and to investigate trace graphs of the sampled parameters. A major limitation of these approaches is that they are developed for continuous parameters and therefore incompatible with a crucial parameter in these inferences: the tree topology. Several recent advancements have aimed at extending these diagnostics to topological space. In this reflection paper, we present two case studies – one on Ebola virus and one on HIV – illustrating how these topological diagnostics can contain information not found in standard diagnostics, and how decisions regarding which of these diagnostics to compute can impact inferences regarding MCMC convergence and mixing. Our results show the importance of running multiple replicate analyses and of carefully assessing topological convergence using the output of these replicate analyses. To this end, we illustrate different ways of assessing and visualizing the topological convergence of these replicates. Given the major importance of detecting convergence and mixing issues in Bayesian phylogenetic analyses, the lack of a unified approach to this problem warrants further action, especially now that additional tools are becoming available to researchers. © 2024, CC BY-SA.

关键词： Phylogenetic

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On a spectral method for β-particle bound excitation collisions in kilonovae

arXiv

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arXiv 2024年

作者： Wollaeger, Ryan T. Fryer, Chris L. Chiodi, Robert Brady, Peter T. Korobkin, Oleg Harnish, Cale Fontes, Christopher J. Haack, Jeffrey R. Chapurin, Oleksandr Koshkarov, Oleksandr Delzanno, Gian Luca Livescu, Daniel Center for Theoretical Astrophysics Los Alamos National Laboratory Los AlamosNM87545 United States Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States Center for Nonlinear Studies Los Alamos National Laboratory Los AlamosNM87545 United States The University of Arizona TucsonAZ85721 United States Department of Physics and Astronomy The University of New Mexico AlbuquerqueNM87131 United States The George Washington University WashingtonDC20052 United States T-5 Applied Mathematics and Plasma Physics Group Los Alamos National Laboratory Los AlamosNM87545 United States Computational Physics Division Los Alamos National Laboratory Los AlamosNM87545 United States

The interaction of β-particles with the weakly ionized plasma background is an important mechanism for powering the kilonova transient signal from neutron star mergers. For this purpose, we present an implementation of the approximate fast-particle collision kernel, described by Inokuti (1971) following the seminal formulation of Bethe (1930), in a spectral solver of the Vlasov-Maxwell-Boltzmann equations. In particular, we expand the fast-particle plane-wave atomic excitation kernel into coefficients of the Hermite basis, and derive the relevant discrete spectral system. In this fast-particle limit, the approach permits the direct use of atomic data, including optical oscillator strengths, normally applied to photon-matter interaction. The resulting spectral matrix is implemented in the MASS-APP spectral solver framework, in a way that avoids full matrix storage per spatial zone. We numerically verify aspects of the matrix construction, and present a proof-of-principle 3D simulation of a 2D axisymmetric kilonova ejecta snapshot. Our preliminary numerical results indicate that a reasonable choice of Hermite basis parameters for β-particles in the kilonova are a bulk velocity parameter u⃗ = 0, a thermal velocity parameter α⃗ = 0.5c, and a 9x9x9 mode velocity basis set (Hermite orders 0 to 8 in each dimension). For ejecta-interior sample zones, we estimate the ratio of thermalization from large-angle ( 2.5◦) bound excitation scattering to total thermalization is ∼0.002-0.003. © 2024, CC BY.

关键词： Boltzmann equation

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