scholarly journals Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids

2020 ◽  
Vol 1 ◽  
Author(s):  
Xiaolong He ◽  
Qizhi He ◽  
Jiun-Shyan Chen ◽  
Usha Sinha ◽  
Shantanu Sinha
Keyword(s):  
Search Algorithm ◽  
Data Driven ◽  
Benchmark Problems ◽  
Local Convexity ◽  
Elastic Solids ◽  
Local Data ◽  
Online Data ◽  
Material Data ◽  
Computer Methods ◽  
Nonlinear Elastic

Abstract As characterization and modeling of complex materials by phenomenological models remains challenging, data-driven computing that performs physical simulations directly from material data has attracted considerable attention. Data-driven computing is a general computational mechanics framework that consists of a physical solver and a material solver, based on which data-driven solutions are obtained through minimization procedures. This work develops a new material solver built upon the local convexity-preserving reconstruction scheme by He and Chen (2020) A physics-constrained data-driven approach based on locally convex reconstruction for noisy database. Computer Methods in Applied Mechanics and Engineering 363, 112791 to model anisotropic nonlinear elastic solids. In this approach, a two-level local data search algorithm for material anisotropy is introduced into the material solver in online data-driven computing. A material anisotropic state characterizing the underlying material orientation is used for the manifold learning projection in the material solver. The performance of the proposed data-driven framework with noiseless and noisy material data is validated by solving two benchmark problems with synthetic material data. The data-driven solutions are compared with the constitutive model-based reference solutions to demonstrate the effectiveness of the proposed methods.

scholarly journals A Multifeatured Data-Driven Homogenization for Heterogeneous Elastic Solids

2021 ◽  
Vol 11 (19) ◽  
pp. 9208
Author(s):  
Ehsan Motevali Haghighi ◽  
Seonhong Na
Keyword(s):  
Finite Element ◽  
Material Properties ◽  
Data Driven ◽  
Porous Solids ◽  
Elastic Solids ◽  
Fe Simulations ◽  
Data Driven Approach ◽  
Micro Level ◽  
Elastic Responses ◽  
Nonlinear Elastic

A computational homogenization of heterogeneous solids is presented based on the data-driven approach for both linear and nonlinear elastic responses. Within the Double-Scale Finite Element Method (FE2) framework, a data-driven model is proposed to substitute the micro-level Finite Element (FE) simulations to reduce computational costs in multiscale simulations. The heterogeneity of porous solids at the micro-level is considered in various material properties and geometrical attributes. For material properties, elastic constants, which are Lame’s coefficients, are subjected to be heterogeneous in the linear elastic responses. For geometrical features, different numbers, sizes, and locations of voids are considered to reflect the heterogeneity of porous solids. A database for homogenized microstructural responses is constructed from a series of micro-level FE simulations, and machine learning is used to train and test our proposed model. In particular, four geometrical descriptors are designed, based on N-probability and lineal-path functions, to clearly reflect the geometrical heterogeneity of various microstructures. This study indicates that a simple deep neural networks model can capture diverse microstructural heterogeneous responses well when given proper input sources, including the geometrical descriptors, are considered to establish a computational data-driven homogenization scheme.

TABU PROGRAMMING: A NEW PROBLEM SOLVER THROUGH ADAPTIVE MEMORY PROGRAMMING OVER TREE DATA STRUCTURES

2011 ◽  
Vol 10 (02) ◽  
pp. 373-406 ◽  
Author(s):  
ABDEL-RAHMAN HEDAR ◽  
EMAD MABROUK ◽  
MASAO FUKUSHIMA
Keyword(s):  
Search Algorithm ◽  
Benchmark Problems ◽  
Problem Solver ◽  
Learning Tools ◽  
Mutation Operators ◽  
Tree Data ◽  
Tree Data Structure ◽  
Comprehensive Framework ◽  
Rate Of Success ◽  
Crossover And Mutation

Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been raised about the complexity of the GP algorithm and the disruption effect of the crossover and mutation operators. In this paper, the Tabu Programming (TP) algorithm is proposed to employ the search strategy of the classical Tabu Search algorithm with the tree data structure. Moreover, the TP algorithm exploits a set of local search procedures over a tree space in order to mitigate the drawbacks of the crossover and mutation operators. Extensive numerical experiments are performed to study the performance of the proposed algorithm for a set of benchmark problems. The results of those experiments show that the TP algorithm compares favorably to recent versions of the GP algorithm in terms of computational efforts and the rate of success. Finally, we present a comprehensive framework called Meta-Heuristics Programming (MHP) as general machine learning tools.

scholarly journals A Modified Harmony Search Algorithm for Solving the Dynamic Vehicle Routing Problem with Time Windows

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Shifeng Chen ◽  
Rong Chen ◽  
Jian Gao
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Search Algorithm ◽  
Time Windows ◽  
Harmony Search ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Dynamic Vehicle Routing ◽  
Dynamic Vehicle Routing Problem ◽  
Routing Problem

The Vehicle Routing Problem (VRP) is a classical combinatorial optimization problem. It is usually modelled in a static fashion; however, in practice, new requests by customers arrive after the initial workday plan is in progress. In this case, routes must be replanned dynamically. This paper investigates the Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) in which customers’ requests either can be known at the beginning of working day or occur dynamically over time. We propose a hybrid heuristic algorithm that combines the harmony search (HS) algorithm and the Variable Neighbourhood Descent (VND) algorithm. It uses the HS to provide global exploration capabilities and uses the VND for its local search capability. In order to prevent premature convergence of the solution, we evaluate the population diversity by using entropy. Computational results on the Lackner benchmark problems show that the proposed algorithm is competitive with the best existing algorithms from the literature.

Developing annotation solutions for online Data Driven Learning

ReCALL ◽  
2009 ◽  
Vol 21 (1) ◽  
pp. 55-75 ◽  
Author(s):  
Pascual Pérez-Paredes ◽  
Jose M. Alcaraz-Calero
Keyword(s):  
Corpus Linguistics ◽  
Language Teaching ◽  
Language Education ◽  
Data Driven ◽  
Online Data ◽  
Analysis And Design ◽  
Language Classroom ◽  
Youth Language ◽  
User Friendly

AbstractAlthough annotation is a widely-researched topic in Corpus Linguistics (CL), its potential role in Data Driven Learning (DDL) has not been addressed in depth by Foreign Language Teaching (FLT) practitioners. Furthermore, most of the research in the use of DDL methods pays little attention to annotation in the design and implementation of corpus-based/driven language teaching.In this paper, we set out to examine the process of development of SACODEYL Annotator, an application that seeks to assist SACODEYL system users in annotating XML multilingual corpora. First, we discuss the role of annotation in DDL and the dominating paradigm in general corpus applications. In the context of the language classroom, we argue that it is essential that corpora should be pedagogically motivated (Braun, 2005 and 2007a). Then, we move on to deal with the analysis and design stages of our annotation solution by illustrating its main features. Some of these include a user friendly hierarchical and extensible taxonomy tree to facilitate the learner-oriented annotation of the corpora; real-time graphics representation of the annotated corpus matching the XML TEI-compliant (Text Encoding Initiative) standard, as well as an intuitive management of the different data sections and associated metadata.SACODEYL (System Aided Compilation and Open Distribution of European Youth Language) is an EU funded MINERVA project which aims to develop an ICT-based system for the assisted compilation and open distribution of multimedia European teen talk in the context of language education. This research lays emphasis on the functionalities of the application within the SACODEYL context. However, our paper addresses similarly the needs of potential multimedia language corpus administrators in general on the lookout for powerful annotation assisting software. SACODEYL Annotator is free to use and can be downloaded from our website.

Synthesis and elastic and mechanical properties of Cr2GeC

2008 ◽  
Vol 23 (8) ◽  
pp. 2157-2165 ◽  
Author(s):  
Shahram Amini ◽  
Aiguo Zhou ◽  
Surojit Gupta ◽  
Andrew DeVillier ◽  
Peter Finkel ◽  
...  
Keyword(s):  
Mechanical Energy ◽  
Kink Band ◽  
Max Phase ◽  
Elastic Solids ◽  
Max Phases ◽  
Kink Bands ◽  
Young’S Moduli ◽  
Microscale Model ◽  
Energy Dissipated ◽  
Nonlinear Elastic

Herein we report on the synthesis and characterization of Cr2GeC, a member of the so-called Mn+1AXn (MAX) phase family of layered machinable carbides and nitrides. Polycrystalline samples were synthesized by hot pressing pure Cr, Ge, and C powders at 1350 °C at ∼45 MPa for 6 h. No peaks other than those associated with Cr2GeC and Cr2O3, in the form of eskolaite, were observed in the x-ray diffraction spectra. The samples were readily machinable and fully dense. The steady-state Vickers hardness was 2.5 ± 0.1 GPa. The Young’s moduli measured in compression and by ultrasound were 200 ± 10 and 245 ± 3 GPa, respectively; the shear modulus and Poisson’s ratio deduced from the ultrasound results were 80 GPa and 0.29, respectively. The ultimate compressive strength for a ∼20 μm grain size sample was 770 ± 30 MPa. Samples compressively loaded from 300 to ∼570 MPa exhibited nonlinear, fully reversible, reproducible, closed hysteretic loops that dissipated ∼20% of the mechanical energy, a characteristic of the MAX phases, in particular, and kinking nonlinear elastic solids, in general. The energy dissipated is presumably due to the formation and annihilation of incipient kink bands. The critical resolved shear stress of the basal plane dislocations—estimated from our microscale model—is ∼22 MPa. The incipient kink band and reversible dislocation densities, at the maximum stress of 568 MPa, are estimated to be 1.2 × 10−2 μm−3 and 1.0 × 1010 cm−2, respectively.

scholarly journals Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1362 ◽  
Author(s):  
Lu Chen ◽  
Na Sun ◽  
Chao Zhou ◽  
Jianzhong Zhou ◽  
Yanlai Zhou ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Flood Control ◽  
Search Algorithm ◽  
Flood Forecasting ◽  
Data Driven ◽  
General Regression Neural Network ◽  
Promising Alternative ◽  
Backtracking Search ◽  
Learning Machine ◽  
Population Based Algorithm

Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the advantages of a faster learning process and stronger generalization ability, has been taken as an effective tool for flood forecasting. However, an ELM model may suffer from local minima in some cases because of its random generation of input weights and hidden layer biases, which results in uncertainties in the flood forecasting model. Therefore, we proposed an improved ELM model for short-term flood forecasting, in which an emerging dual population-based algorithm, named backtracking search algorithm (BSA), was applied to optimize the parameters of ELM. Thus, the proposed method is called ELM-BSA. The upper Yangtze River was selected as a case study. Several performance indexes were used to evaluate the efficiency of the proposed ELM-BSA model. Then the proposed model was compared with the currently used general regression neural network (GRNN) and ELM models. Results show that the ELM-BSA can always provide better results than the GRNN and ELM models in both the training and testing periods. All these results suggest that the proposed ELM-BSA model is a promising alternative technique for flood forecasting.

A Strength Pareto Gravitational Search Algorithm for Multi-Objective Optimization Problems

2015 ◽  
Vol 29 (06) ◽  
pp. 1559010 ◽  
Author(s):  
Xiaohui Yuan ◽  
Zhihuan Chen ◽  
Yanbin Yuan ◽  
Yuehua Huang ◽  
Xiaopan Zhang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Multi Objective ◽  
Fine Grained ◽  
Gravitational Search

A novel strength Pareto gravitational search algorithm (SPGSA) is proposed to solve multi-objective optimization problems. This SPGSA algorithm utilizes the strength Pareto concept to assign the fitness values for agents and uses a fine-grained elitism selection mechanism to keep the population diversity. Furthermore, the recombination operators are modeled in this approach to decrease the possibility of trapping in local optima. Experiments are conducted on a series of benchmark problems that are characterized by difficulties in local optimality, nonuniformity, and nonconvexity. The results show that the proposed SPGSA algorithm performs better in comparison with other related works. On the other hand, the effectiveness of two subtle means added to the GSA are verified, i.e. the fine-grained elitism selection and the use of SBX and PMO operators. Simulation results show that these measures not only improve the convergence ability of original GSA, but also preserve the population diversity adequately, which enables the SPGSA algorithm to have an excellent ability that keeps a desirable balance between the exploitation and exploration so as to accelerate the convergence speed to the true Pareto-optimal front.

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