Artificial neural networks in the calibration of nonlinear mechanical models
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[1] Anna Kucerová,et al. Soft computing-based calibration of microplane M4 model parameters: Methodology and validation , 2013, Adv. Eng. Softw..
[2] David Lehký,et al. ANN inverse analysis based on stochastic small-sample training set simulation , 2006, Eng. Appl. Artif. Intell..
[3] Habib N. Najm,et al. Stochastic spectral methods for efficient Bayesian solution of inverse problems , 2005, J. Comput. Phys..
[4] Patrick van der Smagt,et al. Introduction to neural networks , 1995, The Lancet.
[5] Luoxing Li,et al. Inverse identification of interfacial heat transfer coefficient between the casting and metal mold using neural network , 2010 .
[6] A. Kucerová. Identification of nonlinear mechanical model parameters based on softcomputing methods , 2007 .
[7] Zenon Waszczyszyn,et al. Modal analysis and modified cascade neural networks in identification of geometrical parameters of circular arches , 2011 .
[8] Michael T. Manry,et al. An integrated growing-pruning method for feedforward network training , 2008, Neurocomputing.
[9] Bernhard A. Schrefler,et al. Hygro‐thermo‐chemo‐mechanical modelling of concrete at early ages and beyond. Part I: hydration and hygro‐thermal phenomena , 2006 .
[10] Pietro Lura,et al. Early development of properties in a cement paste: A numerical and experimental study , 2003 .
[11] R. Baierlein. Probability Theory: The Logic of Science , 2004 .
[12] Hermann G. Matthies,et al. Uncertainty Quantification with Stochastic Finite Elements , 2007 .
[13] Joseph A. C. Delaney. Sensitivity analysis , 2018, The African Continental Free Trade Area: Economic and Distributional Effects.
[14] A. Ehrlacher,et al. Analyses and models of the autogenous shrinkage of hardening cement paste: I. Modelling at macroscopic scale , 1995 .
[15] Thomas J. R. Hughes,et al. Encyclopedia of computational mechanics , 2004 .
[16] Albert Tarantola,et al. Inverse problem theory - and methods for model parameter estimation , 2004 .
[17] G. Stavroulakis. 3.13 – Inverse Analysis , 2003 .
[18] J. Shewchuk. An Introduction to the Conjugate Gradient Method Without the Agonizing Pain , 1994 .
[19] Hermann G. Matthies,et al. Parameter Identification in a Probabilistic Setting , 2012, ArXiv.
[20] E. Mizutani,et al. Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.
[21] Ulrich Anders,et al. Model selection in neural networks , 1999, Neural Networks.
[22] John Moody,et al. Prediction Risk and Architecture Selection for Neural Networks , 1994 .
[23] Simon Haykin,et al. Neural Networks and Learning Machines , 2010 .
[24] Raphael T. Haftka,et al. Surrogate-based Analysis and Optimization , 2005 .
[25] Pavel Kordík,et al. Meta-learning approach to neural network optimization , 2010, Neural Networks.
[26] Anna Kucerová,et al. Novel anisotropic continuum-discrete damage model capable of representing localized failure of massive structures. Part II: identification from tests under heterogeneous stress field , 2009, ArXiv.
[27] Hedi Belhadjsalah,et al. Parameter identification of an elasto-plastic behaviour using artificial neural networks–genetic algorithm method , 2011 .
[28] Pavel Kordík. GAME – Hybrid Self-Organizing Modeling System Based on GMDH , 2009 .
[29] G. Liu,et al. Rapid identification of elastic modulus of the interface tissue on dental implants surfaces using reduced-basis method and a neural network. , 2009, Journal of biomechanics.
[30] Kurt Hornik,et al. Some new results on neural network approximation , 1993, Neural Networks.
[31] Anna Kucerová,et al. Competitive Comparison of Optimal Designs of Experiments for Sampling-based Sensitivity Analysis , 2012, ArXiv.
[32] Vít Smilauer,et al. Fuzzy affinity hydration model , 2015, J. Intell. Fuzzy Syst..
[33] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[34] Miguel Cervera,et al. THERMO-CHEMO-MECHANICAL MODEL FOR CONCRETE. I: HYDRATION AND AGING , 1999 .
[35] Roman Lackner,et al. Back analysis of model parameters in geotechnical engineering by means of soft computing , 2003 .
[36] Anna Kučerová,et al. Acceleration of uncertainty updating in the description of transport processes in heterogeneous materials , 2011, J. Comput. Appl. Math..
[37] Godfrey C. Onwubolu. Hybrid Self-Organizing Modeling Systems , 2009 .
[38] Ing Anička Kučerová. Artificial Neural Networks in Calibration of Nonlinear Models , 2012 .
[39] F. Cohen Tenoudji,et al. Mechanical properties of cement pastes and mortars at early ages: Evolution with time and degree of hydration , 1996 .
[40] Martin Abendroth,et al. Identification of ductile damage and fracture parameters from the small punch test using neural networks , 2006 .
[41] Tomáš Mareš,et al. Application of Artificial Neural Networks in Identification of Affinity Hydration Model Parameters , 2012 .
[42] Jon C. Helton,et al. Survey of sampling-based methods for uncertainty and sensitivity analysis , 2006, Reliab. Eng. Syst. Saf..
[43] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.