Stochastic microstructure characterization and reconstruction via supervised learning
暂无分享,去创建一个
Wei Chen | D. Apley | R. Bostanabad | A. Bui | Wei Xie
[1] M. Burge,et al. Digital Image Processing , 2016, Texts in Computer Science.
[2] B. Ripley,et al. Recursive Partitioning and Regression Trees , 2015 .
[3] Xingchen Liu,et al. Random heterogeneous materials via texture synthesis , 2015 .
[4] Marc Secanell,et al. Stochastic reconstruction using multiple correlation functions with different-phase-neighbor-based pixel selection. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.
[5] V. Sundararaghavan. Reconstruction of three-dimensional anisotropic microstructures from two-dimensional micrographs imaged on orthogonal planes , 2014, Integrating Materials and Manufacturing Innovation.
[6] Yang Li,et al. A Descriptor-Based Design Methodology for Developing Heterogeneous Microstructural Materials System , 2014 .
[7] Zhi Xu,et al. Stable-phase method for hierarchical annealing in the reconstruction of porous media images. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.
[8] Yang Li,et al. Stalking the Materials Genome: A Data‐Driven Approach to the Virtual Design of Nanostructured Polymers , 2013, Advanced functional materials.
[9] Z Jiang,et al. Efficient 3D porous microstructure reconstruction via Gaussian random field and hybrid optimization , 2013, Journal of microscopy.
[10] Hongyi Xu,et al. Stochastic Reassembly Strategy for Managing Information Complexity in Heterogeneous Materials Analysis and Design , 2013 .
[11] William B. March,et al. Optimizing the computation of n-point correlations on large-scale astronomical data , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.
[12] David T. Fullwood,et al. Microstructure Sensitive Design for Performance Optimization , 2012 .
[13] Charles H. Ward. Materials Genome Initiative for Global Competitiveness , 2012 .
[14] N. Speybroeck. Classification and regression trees , 2012, International Journal of Public Health.
[15] A. Safekordi,et al. A multiple-point statistics algorithm for 3D pore space reconstruction from 2D images , 2011 .
[16] R. Piasecki,et al. Speeding up of microstructure reconstruction: I. Application to labyrinth patterns , 2011, 1109.3819.
[17] Yuksel C. Yabansu,et al. Understanding and visualizing microstructure and microstructure variance as a stochastic process , 2011 .
[18] Floriana D. Stoian,et al. Prediction of particle size distribution effects on thermal conductivity of particulate composites , 2011 .
[19] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[20] John K Kruschke,et al. Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.
[21] D. Fullwood,et al. Optimized structure based representative volume element sets reflecting the ensemble-averaged 2-point statistics , 2010 .
[22] F. Stillinger,et al. A superior descriptor of random textures and its predictive capacity , 2009, Proceedings of the National Academy of Sciences.
[23] T. Tang,et al. A pixel selection rule based on the number of different‐phase neighbours for the simulated annealing reconstruction of sandstone microstructure , 2009, Journal of microscopy.
[24] D. Fullwood,et al. Gradient-based microstructure reconstructions from distributions using fast Fourier transforms , 2008 .
[25] Xiao-hai He,et al. A hybrid reconstruction method of sandstone from 2D section image , 2008, 2008 International Conference on Neural Networks and Signal Processing.
[26] Philippe H. Geubelle,et al. Reconstruction of periodic unit cells of multimodal random particulate composites using genetic algorithms , 2008 .
[27] D. Fullwood,et al. Microstructure reconstructions from 2-point statistics using phase-recovery algorithms , 2008 .
[28] F. Stillinger,et al. Modeling heterogeneous materials via two-point correlation functions. II. Algorithmic details and applications. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.
[29] F. Stillinger,et al. Modeling heterogeneous materials via two-point correlation functions: basic principles. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.
[30] Ahmed Al-Ostaz,et al. Statistical model for characterizing random microstructure of inclusion–matrix composites , 2007 .
[31] Antonio G. Chessa,et al. A Markov Chain Model for Subsurface Characterization: Theory and Applications , 2006 .
[32] S. Torquato. Necessary Conditions on Realizable Two-Point Correlation Functions of Random Media† , 2006, cond-mat/0606577.
[33] S. Torquato,et al. Random Heterogeneous Materials: Microstructure and Macroscopic Properties , 2005 .
[34] Mingjun Yuan,et al. Microstructure and mechanical properties of microcellular injection molded polyamide-6 nanocomposites , 2005 .
[35] I. Szapudi. Introduction to Higher Order Spatial Statistics in Cosmology , 2005, astro-ph/0505391.
[36] M. Blunt,et al. Pore space reconstruction using multiple-point statistics , 2005 .
[37] Nicholas Zabaras,et al. Classification and reconstruction of three-dimensional microstructures using support vector machines , 2005 .
[38] Asim Tewari,et al. Nearest-neighbor distances between particles of finite size in three-dimensional uniform random microstructures , 2004 .
[39] John W. Crawford,et al. An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure , 2004 .
[40] Mircea Grigoriu,et al. Random field models for two-phase microstructures , 2003 .
[41] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[42] J. Howard,et al. Stochastic reconstruction, 3D characterization and network modeling of chalk , 2002 .
[43] Alexei A. Efros,et al. Image quilting for texture synthesis and transfer , 2001, SIGGRAPH.
[44] S. Ahzi,et al. Statistical continuum theory for large plastic deformation of polycrystalline materials , 2001 .
[45] Marc Levoy,et al. Fast texture synthesis using tree-structured vector quantization , 2000, SIGGRAPH.
[46] G. B. Olson,et al. Designing a New Material World , 2000, Science.
[47] Alexei A. Efros,et al. Texture synthesis by non-parametric sampling , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[48] J. Quintanilla. Microstructure and properties of random heterogeneous materials: A review of theoretical results , 1999 .
[49] S. Torquato,et al. Reconstructing random media. II. Three-dimensional media from two-dimensional cuts , 1998 .
[50] P. Levitz,et al. Off-lattice reconstruction of porous media: critical evaluation, geometrical confinement and molecular transport , 1998 .
[51] G. B. Olson,et al. Computational Design of Hierarchically Structured Materials , 1997 .
[52] Rintoul,et al. Reconstruction of the Structure of Dispersions , 1997, Journal of colloid and interface science.
[53] G. Povirk,et al. Incorporation of microstructural information into models of two-phase materials , 1995 .
[54] R. Tibshirani,et al. An Introduction to the Bootstrap , 1995 .
[55] R R Edelman,et al. Magnetic resonance imaging (1). , 1993, The New England journal of medicine.
[56] I. F. Macdonald,et al. Three‐dimensional reconstruction of porous media from serial section data , 1990 .
[57] J. Quiblier. A new three-dimensional modeling technique for studying porous media , 1984 .
[58] Salvatore Torquato,et al. Microstructure of two-phase random media.III: The n-point matrix probability functions for fully penetrable spheres , 1983 .
[59] B. Efron,et al. A Leisurely Look at the Bootstrap, the Jackknife, and , 1983 .
[60] P. Corson. Correlation functions for predicting properties of heterogeneous materials. I. Experimental measurement of spatial correlation functions in multiphase solids , 1974 .
[61] P. Corson,et al. Correlation functions for predicting properties of heterogeneous materials. II. Empirical construction of spatial correlation functions for two‐phase solids , 1974 .
[62] P. Corson,et al. Correlation functions for predicting properties of heterogeneous materials. IV. Effective thermal conductivity of two‐phase solids , 1974 .
[63] A. M. Bueche,et al. Scattering by an Inhomogeneous Solid , 1949 .
[64] M. Kubát. An Introduction to Machine Learning , 2017, Springer International Publishing.
[65] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[66] Wei Chen,et al. Computational microstructure characterization and reconstruction for stochastic multiscale material design , 2013, Comput. Aided Des..
[67] Xin Sun,et al. Comparison of reconstructed spatial microstructure images using different statistical descriptors , 2012 .
[68] C. Heinzl,et al. Advanced X-Ray Tomographic Methods for Quantitative Characterisation of Carbon Fibre Reinforced Polymers , 2012 .
[69] D. Rypl,et al. Three-Dimensional Reconstruction of Statistically Optimal Unit Cells of Multimodal Particulate Composites , 2010 .
[70] P. Cloetens,et al. X-ray micro-tomography an attractive characterisation technique in materials science , 2003 .
[71] Salvatore Torquato,et al. STATISTICAL DESCRIPTION OF MICROSTRUCTURES , 2002 .
[72] Sebastien Strebelle,et al. Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics , 2002 .
[73] S. Torquato,et al. Reconstructing random media , 1998 .
[74] M. Sumita,et al. Characterization of dispersion state of filler and polymer-filler interactions in rubber-carbon black composites , 1996 .
[75] Yoshua,et al. Pattern Recognition and Neural Networks , 1995 .
[76] J. Hogg. Magnetic resonance imaging. , 1994, Journal of the Royal Naval Medical Service.