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Volumn 86, Issue 21-22, 2008, Pages 1994-2003

Coupling of scales in a multiscale simulation using neural networks

Author keywords

Constitutive modelling; Homogenization; Multilayer perceptron; Neural network; Support vector machines

Indexed keywords

BOUNDARY LAYERS; MULTILAYER NEURAL NETWORKS; NETWORK PROTOCOLS; SENSOR NETWORKS; SUPPORT VECTOR MACHINES; VEGETATION;

EID: 54349126625     PISSN: 00457949     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compstruc.2008.05.004     Document Type: Article
Times cited : (51)

References (28)
  • 1
    • 0030290648 scopus 로고    scopus 로고
    • Acquiring the constitutive relationship for a thermal viscoplastic material using an artificial neural network
    • Qingbin L., Zhong J., Mabao L., and Shichun W. Acquiring the constitutive relationship for a thermal viscoplastic material using an artificial neural network. J Mater Process Technol 62 1-3 (1996) 206-210
    • (1996) J Mater Process Technol , vol.62 , Issue.1-3 , pp. 206-210
    • Qingbin, L.1    Zhong, J.2    Mabao, L.3    Shichun, W.4
  • 2
    • 0032295215 scopus 로고    scopus 로고
    • Modeling of strength of high-performance concrete using artificial neural networks
    • Yeh I. Modeling of strength of high-performance concrete using artificial neural networks. Cement Concrete Res 28 12 (1998) 1797-1808
    • (1998) Cement Concrete Res , vol.28 , Issue.12 , pp. 1797-1808
    • Yeh, I.1
  • 3
    • 0035941122 scopus 로고    scopus 로고
    • A neural network tool for identifying the material parameters of a finite deformation viscoplasticity model with static recovery
    • Huber N., and Tsakmakis C. A neural network tool for identifying the material parameters of a finite deformation viscoplasticity model with static recovery. Comput Methods Appl Mech Eng 191 3-5 (2001) 353-384
    • (2001) Comput Methods Appl Mech Eng , vol.191 , Issue.3-5 , pp. 353-384
    • Huber, N.1    Tsakmakis, C.2
  • 4
    • 33645194644 scopus 로고    scopus 로고
    • Identification of elastic-plastic material parameters from pyramidal indentation of thin films
    • Huber N., Nix W.D., and Gao H. Identification of elastic-plastic material parameters from pyramidal indentation of thin films. Proc Roy Soc - Math Phys Eng Sci A 458 2023 (2002) 1593-1620
    • (2002) Proc Roy Soc - Math Phys Eng Sci A , vol.458 , Issue.2023 , pp. 1593-1620
    • Huber, N.1    Nix, W.D.2    Gao, H.3
  • 5
    • 0041923886 scopus 로고    scopus 로고
    • Back analysis of model parameters in geotechnical engineering by means of soft computing
    • Pichler B., Lackner R., and Mang H.A. Back analysis of model parameters in geotechnical engineering by means of soft computing. Int J Numer Methods Eng 57 14 (2003) 1943-1978
    • (2003) Int J Numer Methods Eng , vol.57 , Issue.14 , pp. 1943-1978
    • Pichler, B.1    Lackner, R.2    Mang, H.A.3
  • 6
    • 34748845259 scopus 로고    scopus 로고
    • Back analysis of microplane model parameters using soft computing methods
    • Kucerova A., Leps M., and Zeman J. Back analysis of microplane model parameters using soft computing methods. Comput Assist Mech Eng Sci 14 2 (2007) 219-242
    • (2007) Comput Assist Mech Eng Sci , vol.14 , Issue.2 , pp. 219-242
    • Kucerova, A.1    Leps, M.2    Zeman, J.3
  • 7
    • 54349104304 scopus 로고    scopus 로고
    • Ghaboussi J, Garret JH, Wu X. Material modeling with neural networks. In: Proceedings of the international conference on numerical methods in engineering: theory and applications, Swansea, UK; 1990. p. 701-17.
    • Ghaboussi J, Garret JH, Wu X. Material modeling with neural networks. In: Proceedings of the international conference on numerical methods in engineering: theory and applications, Swansea, UK; 1990. p. 701-17.
  • 8
    • 0025782169 scopus 로고
    • Knowledge-based modeling of material behavior with neural networks
    • Ghaboussi J., Garret J., and Wu X. Knowledge-based modeling of material behavior with neural networks. J Eng Mech Div, ASCE 117 1 (1991) 132-153
    • (1991) J Eng Mech Div, ASCE , vol.117 , Issue.1 , pp. 132-153
    • Ghaboussi, J.1    Garret, J.2    Wu, X.3
  • 9
    • 0035441570 scopus 로고    scopus 로고
    • Neural networks in mechanics of structures and materials - new results and prospects of applications
    • Waszczyszyn Z., and Ziemianski L. Neural networks in mechanics of structures and materials - new results and prospects of applications. Comput Struct 79 22-25 (2001) 2261-2276
    • (2001) Comput Struct , vol.79 , Issue.22-25 , pp. 2261-2276
    • Waszczyszyn, Z.1    Ziemianski, L.2
  • 10
    • 0032162787 scopus 로고    scopus 로고
    • Implicit constitutive modelling for viscoplasticity using neural networks
    • Furukawa T., and Yagawa G. Implicit constitutive modelling for viscoplasticity using neural networks. Int J Numer Methods Eng 43 2 (1998) 195-219
    • (1998) Int J Numer Methods Eng , vol.43 , Issue.2 , pp. 195-219
    • Furukawa, T.1    Yagawa, G.2
  • 11
    • 0041624274 scopus 로고    scopus 로고
    • Artificial neural network as an incremental non-linear constitutive model for a finite element code
    • Lefik M., and Schrefler B.A. Artificial neural network as an incremental non-linear constitutive model for a finite element code. Comput Methods Appl Mech Eng 192 28-30 (2003) 3265-3283
    • (2003) Comput Methods Appl Mech Eng , vol.192 , Issue.28-30 , pp. 3265-3283
    • Lefik, M.1    Schrefler, B.A.2
  • 12
    • 0031941646 scopus 로고    scopus 로고
    • Constitutive modeling of geomaterials from non-uniform material tests
    • Sidarta D.E., and Ghaboussi J. Constitutive modeling of geomaterials from non-uniform material tests. Comput Geotech 22 1 (1998) 53-71
    • (1998) Comput Geotech , vol.22 , Issue.1 , pp. 53-71
    • Sidarta, D.E.1    Ghaboussi, J.2
  • 13
    • 34848891893 scopus 로고    scopus 로고
    • Integration of laboratory testing and constitutive modeling of soils
    • Fu Q., Hashash Y.M.A., Jung S., and Ghaboussi J. Integration of laboratory testing and constitutive modeling of soils. Comput Geotech 34 5 (2007) 330-345
    • (2007) Comput Geotech , vol.34 , Issue.5 , pp. 330-345
    • Fu, Q.1    Hashash, Y.M.A.2    Jung, S.3    Ghaboussi, J.4
  • 14
    • 0032069843 scopus 로고    scopus 로고
    • Autoprogressive training of neural network constitutive models
    • Ghaboussi J., Pecknold D., Zhang M., and Haj-Ali R. Autoprogressive training of neural network constitutive models. Struct Eng Mech 42 1 (1998) 105-126
    • (1998) Struct Eng Mech , vol.42 , Issue.1 , pp. 105-126
    • Ghaboussi, J.1    Pecknold, D.2    Zhang, M.3    Haj-Ali, R.4
  • 15
    • 0035396126 scopus 로고    scopus 로고
    • Simulated micromechanical models using artificial neural networks
    • Haj-Ali R., Pecknold D.A., Ghaboussi J., and Voyiadjis G.Z. Simulated micromechanical models using artificial neural networks. J Eng Mech 127 7 (2001) 730-738
    • (2001) J Eng Mech , vol.127 , Issue.7 , pp. 730-738
    • Haj-Ali, R.1    Pecknold, D.A.2    Ghaboussi, J.3    Voyiadjis, G.Z.4
  • 16
    • 54349086157 scopus 로고    scopus 로고
    • Unger JF, Könke C. Neural networks as material models within a multiscale approach. In: Topping BH, editor, Proceedings of the ninth international conference on the application of artificial intelligence to civil, structural and environmental engineering, St. Julians, Malta; 2007.
    • Unger JF, Könke C. Neural networks as material models within a multiscale approach. In: Topping BH, editor, Proceedings of the ninth international conference on the application of artificial intelligence to civil, structural and environmental engineering, St. Julians, Malta; 2007.
  • 17
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • Rumelhart D.E., Hinton G.E., and Williams R.J. Learning representations by back-propagating errors. Nature 323 6088 (1986) 533-536
    • (1986) Nature , vol.323 , Issue.6088 , pp. 533-536
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 18
    • 84943274699 scopus 로고    scopus 로고
    • Riedmiller M, Braun H. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the IEEE international conference on neural networks, San Francisco (CA); 1993. p. 586-91.
    • Riedmiller M, Braun H. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the IEEE international conference on neural networks, San Francisco (CA); 1993. p. 586-91.
  • 19
    • 0000293377 scopus 로고
    • Backpropagation learning for multilayer feed-forward neural networks using the conjugate gradient method
    • Johansson E.M., Dowla F.U., and Goodman D.M. Backpropagation learning for multilayer feed-forward neural networks using the conjugate gradient method. Int J Neural Syst 2 4 (1992) 291-301
    • (1992) Int J Neural Syst , vol.2 , Issue.4 , pp. 291-301
    • Johansson, E.M.1    Dowla, F.U.2    Goodman, D.M.3
  • 20
    • 0027205884 scopus 로고
    • Scaled conjugate gradient algorithm for fast supervised learning
    • Moller M.F. Scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6 4 (1993) 525-533
    • (1993) Neural Networks , vol.6 , Issue.4 , pp. 525-533
    • Moller, M.F.1
  • 21
    • 0028543366 scopus 로고
    • Training feedforward networks with the Marquardt algorithm
    • Hagan M.H., and Menhaj M.B. Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Networks 5 6 (1994) 989-993
    • (1994) IEEE Trans Neural Networks , vol.5 , Issue.6 , pp. 989-993
    • Hagan, M.H.1    Menhaj, M.B.2
  • 25
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • Burges C.J. A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2 2 (1998) 121-167
    • (1998) Data Min Knowl Disc , vol.2 , Issue.2 , pp. 121-167
    • Burges, C.J.1
  • 26
    • 0000557604 scopus 로고
    • A duality theorem for nonlinear programming
    • Wolfe P. A duality theorem for nonlinear programming. Quart Appl Math 19 3 (1961) 239-244
    • (1961) Quart Appl Math , vol.19 , Issue.3 , pp. 239-244
    • Wolfe, P.1
  • 27
    • 0003798627 scopus 로고    scopus 로고
    • MIT Press [Chapter: Fast training of support vector machines using sequential minimal optimization] p. 185-208
    • Platt J.C. Advances in kernel methods: support vector learning (1999), MIT Press [Chapter: Fast training of support vector machines using sequential minimal optimization] p. 185-208
    • (1999) Advances in kernel methods: support vector learning
    • Platt, J.C.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.