检索结果-内蒙古大学图书馆

Viscosity in water from first-principles and deep-neural-network simulations

学校读者我要写书评

暂无评论

npj Computational Materials 2022年第1期8卷 1318-1327页

作者： Cesare Malosso Linfeng Zhang Roberto Car Stefano Baroni Davide Tisi SISSA—Scuola Internazionale Superiore di Studi Avanzati 34136TriesteItaly Program in Applied and Computational Mathematics Princeton UniversityPrincetonNJ08544USA DP Technology Beijing100080PR China Department of Chemistry Department of Physicsand Princeton Institute for the Science and Technology of MaterialsPrinceton UniversityPrincetonNJUSA CNR Istituto Officina dei Materiali SISSA Unit34136TriesteItaly

We report on an extensive study of the viscosity of liquid water at near-ambient conditions,performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics(AIMD),based on density-functional theory(DFT).In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy,our ab initio approach is enhanced with deep-neural-network potentials(NNP).This approach is first validated against AIMD results,obtained by using the Perdew–Burke–Ernzerhof(PBE)exchange-correlation functional and paying careful attention to crucial,yet often overlooked,aspects of the statistical data ***,we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed(SCAN)*** the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one,our SCAN predictions of the shear viscosity of water are in very good agreement with experiments.

关键词： viscosity Strongly ambient

Corrigendum to “Non-linear singularity formation for circular vortex sheets”

学校读者我要写书评

暂无评论

Quarterly of applied mathematics 2024年第2期82卷 449-450页

作者： Murray, Ryan Wilcox, Galen Department of Mathematics North Carolina State University Raleigh NC 27607 MIT-WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering Cambridge MA 02139 and Woods Hole MA 02543

Abstract: This paper serves as a short corrigendum, describing an error in the derivation of the linearized equation. The main theorems remain correct, but they are based upon an incorrect linearization of the Birkhoff-Rott equation and hence are not directly applicable to the original physical problem.

关键词：

Bibliometric Analysis: Research Trends in Leaf Image Segmentation and Classification

学校读者我要写书评

暂无评论

Bibliometric Analysis: Research Trends in Leaf Image Segment...

Artificial Intelligence and Mechatronics Systems (AIMS), International Conference on

作者： Eka Puji Agustini Rahmad Gernowo Kurnianingsih Adi Wibowo Budi Warsito Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Doctoral Program of Information System Diponegoro University Semarang Indonesia Master of Applied Science Program Polytechnic Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro Universitty Semarang Indonesia

Exploring various phenomena and issues related to leaf images is paramount, particularly in segmentation and classification of such images. This study employs bibliometric analysis to delve into two overarching themes: the trends in publication and the evolution of publications, along with a keyword-based analysis. The research methodology unfolds in two stages: (1) data collection and (2) analysis. The dataset comprises 1,248 articles sourced from the Scopus database, covering the period from 1988 to 2023. The research findings unveil a noteworthy surge in publication trends, peaking at 231 documents in 2023. An in-depth examination of journal names demonstrates that studies on the segmentation and classification of leaf images are predominantly featured in computer science journals, exemplified by the publication of 589 documents. Furthermore, an analysis of frequently used keywords highlights “Extraction” as the predominant term, employed a total of 364 times. This underscores that the research focus on leaf image segmentation and classification presents ample opportunities for researchers to delve more profoundly into the subject.

关键词：

Asymptotic perpendicular transport in low-beta collisionless plasma

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Coughlin, Jack Hu, Jingwei Shumlak, Uri Department of Applied Mathematics University of Washington SeattleWA98195 United States Aerospace and Energetics Research Program University of Washington SeattleWA98195 United States

Kinetic physics, including finite Larmor radius (FLR) effects, are known to affect the physics of magnetized plasma phenomena such as the Kelvin-Helmholtz and Rayleigh-Taylor instabilities. Accurately incorporating FLR effects into fluid simulations requires moment closures for the heat flux and stress tensor, including the gyroviscous stress in collisionless magnetized plasmas. However, the most commonly used gyroviscous stress tensor closure (Braginskii Rev. Plasma Phys., 1965) is based on a strongly collisional assumption for the asymptotic expansion of the kinetic equation in the so-called fast-dynamics ordering. This collisional assumption becomes less valid for some high-temperature plasmas. To explore perpendicular transport in collisionless and weakly collisional plasmas, an asymptotic analysis of the weakly collisional Vlasov equation in the slow-dynamics or drift ordering is performed in a new "semi-fluid" formalism, which integrates in v ⊥ to obtain a five-moment system. The associated heat flux and stress tensor closures are determined via a Hilbert expansion of the kinetic equation. A numerically affordable approximation to the stress tensor is proposed which adjusts the Braginskii closure to account for temperature gradient-driven stress. Continuum kinetic simulations of a family of sheared-flow configurations with variable magnetization and temperature gradients are performed to validate the drift ordering semi-fluid expansion. The expected convergence with magnetization is observed, and residuals are examined and discussed in terms of their relationship to higher-order terms in the expansion. The adjusted Braginskii closure is found to accurately correct for the error committed by the Braginskii gyroviscous stress tensor closure in the presence of temperature *** Codes 35Q83, 35Q35, 76M22, 76M20, 76W05 © 2024, CC BY.

关键词： Heat flux

A FLOW ARTIST FOR HIGH-DIMENSIONAL CELLULAR DATA

学校读者我要写书评

暂无评论

arXiv 2023年

作者： MacDonald, Kincaid Bhaskar, Dhananjay Thampakkul, Guy Nguyen, Nhi Zhang, Joia Perlmutter, Michael Adelstein, Ian Krishnaswamy, Smita Department of Mathematics Yale University United States Department of Genetics Yale School of Medicine United States Department of Computer Science Yale University United States Department of Mathematics Pomona College United States Applied Mathematics Program Yale University United States Department of Statistics University of Washington United States Department of Mathematics Boise State University United States Computational Biology and Bioinformatics Program Yale University United States

We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured, including in high-throughput biology such as single-cell transcriptomics. Existing embedding techniques either do not utilize velocity information or embed the coordinates and velocities independently, i.e., they either impose velocities on top of an existing point embedding or embed points within a prescribed vector field. Here we present FlowArtist, a neural network that embeds points while jointly learning a vector field around the points. The combination allows FlowArtist to better separate and visualize velocity-informed structures. Our results, on toy datasets and single-cell RNA velocity data, illustrate the value of utilizing coordinate and velocity information in tandem for embedding and visualizing high-dimensional data. © 2023, CC BY.

关键词： RNA

Machine learning of microstructure–property relationships in materials with robust features from foundational vision transformers

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Whitman, Sheila E. Latypov, Marat I. Graduate Interdisciplinary Program in Applied Mathematics University of Arizona TucsonAZ85721 United States Department of Materials Science and Engineering University of Arizona TucsonAZ85721 United States

Machine learning of microstructure–property relationships from data is an emerging approach in computational materials science. Most existing machine learning efforts focus on the development of task-specific models for each microstructure–property relationship. We propose utilizing pre-trained foundational vision transformers for the extraction of task-agnostic microstructure features and subsequent light-weight machine learning of a microstructure-dependent property. We demonstrate our approach with pre-trained state-of-the-art vision transformers (CLIP, DINOV2, SAM) in two case studies on machine-learning: (i) elastic modulus of two-phase microstructures based on simulations data;and (ii) Vicker’s hardness of Ni-base and Co-base superalloys based on experimental data published in literature. Our results show the potential of foundational vision transformers for robust microstructure representation and efficient machine learning of microstructure–property relationships without the need for expensive task-specific training or fine-tuning of bespoke deep learning models. © 2025, CC BY.

关键词： Elastic moduli

BLIS-Net: Classifying and Analyzing Signals on Graphs

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Xu, Charles Goldman, Laney Guo, Valentina Hollander-Bodie, Benjamin Trank-Greene, Maedee Adelstein, Ian De Brouwer, Edward Ying, Rex Krishnaswamy, Smita Perlmutter, Michael Department of Mathematics Yale University United States Department of Computer Science Yale University United States Department of Mathematics Harvey Mudd College United States Department of Genetics Yale School of Medicine United States Department of Applied Mathematics University of Colorado Boulder United States Department of Mathematics Boise State University United States Program in Computing Boise State University United States

Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. However, much less work has been done on signal classification, where the data consists of many functions (referred to as signals) defined on the vertices of a single graph. These tasks require networks designed differently from those designed for traditional GNN tasks. Indeed, traditional GNNs rely on localized low-pass filters, and signals of interest may have intricate multi-frequency behavior and exhibit long range interactions. This motivates us to introduce the BLIS-Net (Bi-Lipschitz Scattering Net), a novel GNN that builds on the previously introduced geometric scattering transform. Our network is able to capture both local and global signal structure and is able to capture both low-frequency and high-frequency information. We make several crucial changes to the original geometric scattering architecture which we prove increase the ability of our network to capture information about the input signal and show that BLIS-Net achieves superior performance on both synthetic and real-world data sets based on traffic flow and fMRI data. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

Accurate Formula for the Effective Conductivity of Highly Clustered Two-Phase Materials

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Skolnick, Murray Torquato, Salvatore Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Princeton Materials Institute and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Two-phase heterogeneous materials arising in a variety of natural and synthetic situations exhibit a wide-variety of microstructures and thus display a broad-spectrum effective physical properties. Given that such properties of disordered materials generally depend on an infinite-set of microstructural correlation functions that are typically unobtainable, in practice, one must consider rigorous bounds and approximations on the effective properties that depend on a limited set of nontrivial microstructural information. Torquato derived [Random Heterogeneous Materials, Microstructure and Macroscopic Properties (2002)] such an approximation for the effective thermal/electrical conductivity of two-phase microstructures that perturb about those that realize the well-known self-consistent formula;a key feature of such microstructures being phase-inversion symmetry which can be observed in certain cellular, bicontinuous, and porous materials. Here, we show via extensive numerical simulations that this virtually unexamined approximation, which depends on up to three-point correlations, predicts accurately the effective conductivities of various two- and three-dimensional two-phase microstructures across phase volume fractions in which both phases can form large and well-connected clusters;including certain phase-inversion symmetric microstructures as well as phase-inversion asymmetric dispersions of certain fully penetrable particles. We also highlight the accurate sensitivity of this approximation to phase-connectedness properties by showing that it predicts percolation thresholds that are in good quantitative and/or qualitative agreement with known thresholds for the models considered here. Finally, we discuss how this formula can be used to design materials with desirable effective conductivity properties and thus aid in materials by design. © 2025, CC BY-SA.

关键词： Porous materials

Lung Cancer Diagnosis Based on Object Detection Technology 14th

学校读者我要写书评

暂无评论

Lung Cancer Diagnosis Based on Object Detection Technology

14th International Conference on Frontier Computing, FC 2024

作者： Xu, Jia-Lang Wang, Pei-Yun Huang, Tzu-Hsuan Hsu, Ying-Lin Huang, Kuo-Yang Chen, Mu-Yen Artificial Intelligence Development Center Changhua Christian Hospital Changhua Taiwan Doctoral Program in Data Science and Industrial Analytics National Chung Hsing University Taichung Taiwan Department of Applied Mathematics and Institute of Statistics National Chung Hsing University Taichung Taiwan Division of Chest Medicine Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan Department of Engineering Science National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9789819623945

With the rise of artificial intelligence, many people nowadays use artificial intelligence to help solve some problems in life, and the medical field is also with the rise of artificial intelligence, many people are slowly applying artificial intelligence to medical images to assist in decision-making, this study proposes a Yolov8 algorithm for object tracking of CT images of lung cancer, which can quickly tell the doctor where the cancer is and what the type of cancer is. The results of this study show that the Box_Loss, Cls_Loss, and Dfl_loss of the Yolov8 algorithm in detecting the location of lung cancer are 1.3689, 0.4552, and 1.5569 in the validation data, respectively, and thus this study suggests that this method can help physicians know the location of the tumor and what kind of tumor it belongs to. Therefore, this study suggests that this method can help physicians know the location of the tumor and the type of tumor. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer