检索结果-内蒙古大学图书馆

5th International Conference on Information Science and Control Engineering (ICISCE)

作者： Hu, Nan Zhou, Xuehai Li, Xi Wang, Chao Univ Sci & Technol China Dept Comp Sci Hefei Anhui Peoples R China

ISBN: (纸本)9781538655009

FPGA is widely adopted in computing acceleration. In recent years, the embedded GPU on mobile SoC supports OpenCL development which allows GPU to be used in the general-purpose computing. However, the study of cooperation of FPGA and SoC-based mobile GPU in an embedded system is still lacking. We propose methods of cooperation of the FPGA and GPU, which supports software and hardware task partition and unified development by OpenCL. We build an embedded system the storage oscilloscope which supports 3D waveform display. The performance of algorithms such as string matching, matrix operations, histogram, grayscale stretching, are analyzed on the ARM processor, GPU and FPGA. The results show that the coupled FPGA-GPU embedded system achieves approached performance to the pure FPGA with no more than 15% DSP slice resource.

关键词： reconfigurable computing data-driven computing GPU and FPGA cooperation embedded system ARM GPU FPGA benchmark 3D waveforms

来源：评论

学校读者我要写书评

暂无评论

data-driven Approach to Inversion Analysis of Three-Dimensional Inner Soil Structure via Wave Propagation Analysis 20th

Data-Driven Approach to Inversion Analysis of Three-Dimensio...

引用

20th Annual International Conference on Computational Science (ICCS)

作者： Yamaguchi, Takuma Ichimura, Tsuyoshi Fujita, Kohei Hori, Muneo Wijerathne, Lalith Ueda, Naonori Univ Tokyo Earthquake Res Inst Bunkyo Ku Tokyo Japan Univ Tokyo Dept Civil Engn Bunkyo Ku Tokyo Japan Japan Agcy Marine Earth Sci & Technol Res Inst Value Added Informat Generat Yokohama Kanagawa Japan RIKEN Ctr Adv Intelligence Project Tokyo Japan

ISBN: (纸本)9783030504205;9783030504199

Various approaches based on both computational science and data science/machine learning have been proposed with the development of observation systems and network technologies. Computation cost associated with computational science can be reduced by introducing the methods based on data science/machine learning. In the present paper, we focus on a method to estimate inner soil structure via wave propagation analysis. It is regarded as one of the parameter optimization approaches using observation data on the surface. This application is in great demand to ensure better reliability in numerical simulations. Typical optimization requires many forward analyses;thus, massive computation cost is required. We propose an approach to substitute evaluation using neural networks for most cases of forward analyses and to reduce the number of forward analyses. Forward analyses in the proposed method are used for producing the training data for a neural network;thereby they can be computed independently, and the actual elapsed time can be reduced by using a large-scale supercomputer. We demonstrated that the inner soil structure was estimated with the sufficient accuracy for practical damage evaluation. We also confirmed that the proposed method achieved estimating parameters within a shorter time-frame compared to a typical approach based on simulated annealing.

关键词： data-driven computing Finite element analysis Conjugate gradient method GPU computing

来源：评论

学校读者我要写书评

暂无评论

Non-parametric data-driven approach to reliability-based topology optimization of trusses under uncertainty of material constitutive law

引用

JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2024年第5期18卷

作者： Kanno, Yoshihiro Univ Tokyo Math & Informat Ctr Hongo 7-3-1 Tokyo 1138656 Japan

The material behavior intrinsically possesses the aleatory uncertainty (i.e., the natural variability). Against uncertainty in a given data set of elastic material responses, this paper presents a data-driven approach to reliability- based truss topology optimization under the compliance constraint. We utilize the order statistics to guarantee the confidence level of the probability that the reliability on the compliance constraint is no smaller than the target reliability, and formulate the truss optimization problem in a bi-level optimization form. By using the duality of linear optimization, we recast this bi-level optimization problem as a single-level optimization problem, which can be solved with a standard nonlinear optimization approach. Numerical examples illustrate the validity, as well as the characteristic of optimal solutions, of the proposed method.

关键词： Reliability design Uncertain input distribution data-driven computing Bi-level optimization Duality

来源：评论

学校读者我要写书评

暂无评论

data-driven model-free modified nodal analysis circuit solver

引用

INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2024年第2期37卷

作者： Galetzka, Armin Loukrezis, Dimitrios De Gersem, Herbert Tech Univ Darmstadt Inst Accelerator Sci & Electromagnet Fields Darmstadt Germany Tech Univ Darmstadt Grad Sch Computat Engn Darmstadt Germany Schlossgartenstr 8 Darmstadt Germany

This work introduces a novel data-driven model-free modified nodal analysis (MNA) circuit solver. The solver is capable of handling circuit problems featuring elements for which solely measurement data are available. Rather than utilizing hard-coded phenomenological model representations, the data-driven MNA solver reformulates the circuit problem such that the solution is found by minimizing the distance between circuit states that fulfill Kirchhoff's laws, and states belonging to the measurement data. In this way, the formerly inevitable demand for model representations is eliminated, thus avoiding the introduction of related modeling errors and uncertainties. The proposed solver is applied to linear and nonlinear RC-circuits and to a half-wave rectifier.

关键词： circuit simulation data-driven computing model-free solver modified nodal analysis

来源：评论

学校读者我要写书评

暂无评论

data-driven nonlocal mechanics: Discovering the internal length scales of materials

引用

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021年 386卷 114039-114039页

作者： Karapiperis, K. Ortiz, M. Andrade, J. E. Swiss Fed Inst Technol Mech & Mat Lab Dept Mech & Proc Engn CH-8092 Zurich Switzerland CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA CALTECH Grad Aerosp Labs Pasadena CA 91125 USA

Nonlocal effects permeate most microstructured materials, including granular media, metals and foams. The quest for predictive nonlocal mechanical theories with well-defined internal length scales has been ongoing for more than a century since the seminal work of the Cosserats. We present here a novel framework for the nonlocal analysis of material behavior, which bypasses the need to define any internal length scale. This is achieved by extending the data-driven paradigm in mechanics, originally introduced for simple continua, into generalized continua. The problem is formulated directly on a material data set, comprised of higher-order kinematics and their conjugate kinetics, which are identified from experiments or inferred from lower scale computations. The case of a micropolar continuum is used as a vehicle to introduce the framework, which may also be adapted to strain-gradient and micromorphic media. Two applications are presented: a micropolar elastic plate with a hole, which is used to demonstrate the convergence properties of the method, and the shear banding problem of a triaxially compressed sample of quartz sand, which is used to demonstrate the applicability of the method in the case of complex history-dependent material behavior. (C) 2021 Published by Elsevier B.V.

关键词： data-driven computing Nonlocal mechanics Micropolar continuum Granular materials

来源：评论

学校读者我要写书评

暂无评论

Efficient data structures for model-free data-driven computational mechanics

引用

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021年 382卷 113855-113855页

作者： Eggersmann, Robert Stainier, Laurent Ortiz, Michael Reese, Stefanie Rhein Westfal TH Aachen Inst Appl Mech Mies van der Rohe Str 1 D-52074 Aachen Germany Ecole Cent Nantes Inst Civil & Mech Engn 1 Rue Noe F-44321 Nantes France CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA Rhein Friedrich Wilhelm Univ Bonn Hausdorff Ctr Math Endenicher Allee 62 D-53115 Bonn Germany

The data-driven computing paradigm initially introduced by Kirchdoerfer & Ortiz (2016) enables finite element computations in solid mechanics to be performed directly from material data sets, without an explicit material model. From a computational effort point of view, the most challenging task is the projection of admissible states at material points onto their closest states in the material data set. In this study, we compare and develop several possible data structures for solving the nearest-neighbor problem. We show that approximate nearest-neighbor (ANN) algorithms can accelerate material data searches by several orders of magnitude relative to exact searching algorithms. The approximations are suggested by-and adapted to-the structure of the data-driven iterative solver and result in no significant loss of solution accuracy. We assess the performance of the ANN algorithm with respect to material data set size with the aid of a 3D elasticity test case. We show that computations on a single processor with up to one billion material data points are feasible within a few seconds execution time with a speed up of more than 106 with respect to exact k-d trees. (C) 2021 Published by ElsevierB.V.

关键词： data-driven computing Solid mechanics Nearest neighbor problem Approximate nearest-neighbor search data structures data science

来源：评论

学校读者我要写书评

暂无评论

Interatomic-Potential-Free, data-driven Molecular Dynamics

引用

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2023年 415卷

作者： Bulin, J. Hamaekers, J. Ariza, M. P. Ortiz, M. Fraunhofer Inst Algorithms & Sci Comp Schloss Birlinghoven D-53757 St Augustin Germany Univ Seville Escuela Tecn Super Ingn Seville 41092 Spain Univ Bonn Hausdorff Ctr Math Endenicher Allee 60 D-53115 Bonn Germany CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA

We present a data-driven (DD) paradigm that enables molecular dynamics calculations to be performed directly from sampled force-field data such as obtained, e. g., from ab initio calculations, thereby eschewing the conventional step of modeling the data by empirical interatomic potentials entirely. The data required by the DD solvers consists of local atomic configurations and corresponding atomic forces and is, therefore, fundamental, i. e., it is not beholden to any particular model. The resulting DD solvers, including a fully explicit DD-Verlet algorithm, are provably convergent and exhibit robust convergence with respect to the data in selected test cases. We present an example of application to C60 buckminsterfullerenes that showcases the feasibility, range and scope of the DD molecular dynamics paradigm.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： data-driven computing Molecular dynamics Optimal control Game theory Wasserstein metric

来源：评论

学校读者我要写书评

暂无评论

Model-free data-driven computational mechanics enhanced by tensor voting

引用

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021年 373卷 113499-113499页

The data-driven computing paradigm initially introduced by Kirchdoerfer and Ortiz (2016) is extended by incorporating locally linear tangent spaces into the data set. These tangent spaces are constructed by means of the tensor voting method introduced by Mordohai and Medioni (2010) which improves the learning of the underlying structure of a data set. Tensor voting is an instance-based machine learning technique which accumulates votes from the nearest neighbors to build up second-order tensors encoding tangents and normals to the underlying data structure. The here proposed second-order data-driven paradigm is a plug-in method for distance-minimizing as well as entropy-maximizing data-driven schemes. Like its predecessor (Kirchdoerfer and Ortiz, 2016), the resulting method aims to minimize a suitably defined free energy over phase space subject to compatibility and equilibrium constraints. The method's implementation is straightforward and numerically efficient since the data structure analysis is performed in an offline step. Selected numerical examples are presented that establish the higher-order convergence properties of the data-driven solvers enhanced by tensor voting for ideal and noisy data sets. (C) 2020 Elsevier B.V. All rights reserved.

关键词： data-driven computing Tensor voting Second-order method data science

来源：评论

学校读者我要写书评

暂无评论

Finite element solver for data-driven finite strain elasticity

引用

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021年 379卷 113756-113756页

作者： Platzer, Auriane Leygue, Adrien Stainier, Laurent Ortiz, Michael Univ Nantes Ecole Cent Nantes CNRS Inst Rech Genie Civil & Mecan GeMUMR 6183 1 Rue Noe F-44321 Nantes France CALTECH Grad Aerosp Labs 1200 E Calif Blvd Pasadena CA 91125 USA

A nominal finite element solver is proposed for data-driven finite strain elasticity. It bypasses the need for a constitutive model by considering a database of deformation gradient/first Piola-Kirchhoff stress tensors pairs. The boundary value problem is reformulated as the constrained minimization problem of the distance between (i) the mechanical states, i.e. strain-stress, in the body and (ii) the material states coming from the database. The corresponding constraints are of two types: kinematical, i.e. displacement-strain relation, and mechanical, i.e. conservation linear and angular momenta. The solver uses alternated minimization: the material states are determined from a local search in the database using an efficient tree-based nearest neighbor search algorithm, and the mechanical states result from a standard constrained minimization addressed with an augmented Lagrangian approach. The performance of the solver is demonstrated by means of 2D sanity check examples: the data-driven solution converges to the classical finite element solution when the material database increasingly approximates the constitutive model. In addition, we demonstrate that the balance of angular momentum, which was classically not taken into account in previous data-driven studies, must be enforced as a constraint to ensure the convergence of the method. (C) 2021 Elsevier B.V. All rights reserved.

关键词： data-driven computing Finite strain Model-free Optimization methods data science

来源：评论

学校读者我要写书评

暂无评论

A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems

引用

JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME 2022年第12期144卷 121006-121006页

作者： Taneja, Karan He, Xiaolong He, QiZhi Zhao, Xinlun Lin, Yun-An Loh, Kenneth J. Chen, Jiun-Shyan Univ Calif San Diego Dept Struct Engn La Jolla CA 92093 USA Univ Minnesota Dept Civil Environm & Geoengn Minneapolis MN 55455 USA

Identification of muscle-tendon force generation properties and muscle activities from physiological measurements, e.g., motion data and raw surface electromyography (sEMG), offers opportunities to construct a subject-specific musculoskeletal (MSK) digital twin system for health condition assessment and motion prediction. While machine learning approaches with capabilities in extracting complex features and patterns from a large amount of data have been applied to motion prediction given sEMG signals, the learned data-driven mapping is black-box and may not satisfy the underlying physics and has reduced generality. In this work, we propose a feature-encoded physics-informed parameter identification neural network (FEPI-PINN) for simultaneous prediction of motion and parameter identification of human MSK systems. In this approach, features of high-dimensional noisy sEMG signals are projected onto a low-dimensional noise-filtered embedding space for the enhancement of forwarding dynamics prediction. This FEPI-PINN model can be trained to relate sEMG signals to joint motion and simultaneously identify key MSK parameters. The numerical examples demonstrate that the proposed framework can effectively identify subject-specific muscle parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion predictions of elbow flexion-extension motion that are in good agreement with the measured joint motion data.

关键词： physics-informed neural networks parameter identification musculoskeletal system data-driven computing feature-encoding surface electromyography

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：