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[해외논문] Deep learned finite elements

Computer methods in applied mechanics and engineering, v.372, 2020년, pp.113401 -   

Jung, Jaeho (Korea Atomic Energy Research Institute) ,  Yoon, Kyungho (Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology) ,  Lee, Phill-Seung (Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology)

Abstract AI-Helper 아이콘AI-Helper

Abstract In this paper, we propose a method that employs deep learning, an artificial intelligence technique, to generate stiffness matrices of finite elements. The proposed method is used to develop 4- and 8-node 2D solid finite elements. The deep learned finite elements practically pass the patch...

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참고문헌 (54)

  1. Bathe 2006 Finite Element Procedures 

  2. Jin 2015 The Finite Element Method in Electromagnetics 

  3. Gresho 1998 Incompressible Flow and the Finite Element Method. Volume 1: Advection-Diffusion and Isothermal Laminar Flow 

  4. Lesaint 1974 On a Finite Element Method for Solving the Neutron Transport Equation 

  5. Comput. Methods Appl. Mech. Engrg. Donea 33 689 1982 10.1016/0045-7825(82)90128-1 An arbitrary Lagrangian-Eulerian finite element method for transient dynamic fluid-structure interactions 

  6. Volakis 1998 Finite Element Method Electromagnetics: Antennas, Microwave Circuits, and Scattering Applications 

  7. Reddy 2010 The Finite Element Method in Heat Transfer and Fluid Dynamics 

  8. Girault 1979 Finite Element Approximation of the Navier-Stokes Equations 

  9. Bull. Math. Biophys. McCulloch 5 115 1943 10.1007/BF02478259 A logical calculus of the ideas immanent in nervous activity 

  10. Psychol. Rev. Rosenblatt 65 386 1958 10.1037/h0042519 The perceptron: a probabilistic model for information storage and organization in the brain 

  11. Neural Comput. Hinton 18 1527 2006 10.1162/neco.2006.18.7.1527 A fast learning algorithm for deep belief nets 

  12. Nature Rumelhart 323 533 1986 10.1038/323533a0 Learning representations by back-propagating errors 

  13. Krizhevsky 1097 2012 Advances in Neural Information Processing Systems Imagenet classification with deep convolutional neural networks 

  14. Nature Silver 529 484 2016 10.1038/nature16961 Mastering the game of Go with deep neural networks and tree search 

  15. Nature Silver 550 354 2017 10.1038/nature24270 Mastering the game of Go without human knowledge 

  16. J. Comput. Phys. Sirignano 375 1339 2018 10.1016/j.jcp.2018.08.029 DGM: a deep learning algorithm for solving partial differential equations 

  17. J. Mach. Learn. Res. Raissi 19 932 2018 Deep hidden physics models: deep learning of nonlinear partial differential equations 

  18. J. Comput. Phys. Raissi 378 686 2019 10.1016/j.jcp.2018.10.045 Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations 

  19. J. Tompson, K. Schlachter, P. Sprechmann, K. Perlin, Accelerating Eulerian fluid simulation with convolutional networks, in: Proceedings of the 34th International Conference on Machine Learning, Vol. 70, 2017, pp. 3424-3433. 

  20. 10.1145/2939672.2939738 X. Guo, W. Li, F. Iorio, Convolutional neural networks for steady flow approximation, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 481-490. 

  21. Hennigh 2017 Lat-net: compressing lattice Boltzmann flow simulations using deep neural networks 

  22. J. Fluid Mech. Ling 807 155 2016 10.1017/jfm.2016.615 Reynolds averaged turbulence modelling using deep neural networks with embedded invariance 

  23. Zhang 2460 2015 22nd AIAA Computational Fluid Dynamics Conference Machine learning methods for data-driven turbulence modeling 

  24. Beck 2018 Deep neural networks for data-driven turbulence models 

  25. Neural Netw. Takeuchi 7 389 1994 10.1016/0893-6080(94)90031-0 Neural network representation of finite element method 

  26. J. R. Soc. Interface Liang 15 2018 10.1098/rsif.2017.0844 A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis 

  27. Int. J. Adv. Manuf. Technol. Chamekh 44 173 2009 10.1007/s00170-008-1809-6 Inverse technique identification of material parameters using finite element and neural network computation 

  28. Internat. J. Numer. Methods Engrg. Hashash 59 989 2004 10.1002/nme.905 Numerical implementation of a neural network based material model in finite element analysis 

  29. ACM Trans. Graph. Chen 34 74 2015 10.1145/2766889 Data-driven finite elements for geometry and material design 

  30. Comput. Methods Appl. Mech. Engrg. Oishi 327 327 2017 10.1016/j.cma.2017.08.040 Computational mechanics enhanced by deep learning 

  31. Zienkiewicz 2000 The Finite Element Method: The Basis 

  32. Neural Netw. Hornik 2 359 1989 10.1016/0893-6080(89)90020-8 Multilayer feedforward networks are universal approximators 

  33. Neural Comput. Le Roux 22 2192 2010 10.1162/neco.2010.08-09-1081 Deep belief networks are compact universal approximators 

  34. Abadi 265 2016 12th USENIX Symposium on Operating Systems Design and Implementation TensorFlow: a system for large-scale machine learning 

  35. Kingma 2014 Adam: a method for stochastic optimization 

  36. X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010, pp. 249-256. 

  37. Roylance 2001 Transformation of Stresses and Strains 

  38. Zienkiewicz 1989 The Finite Element Method: Basic Formulation and Linear Problems 

  39. Comput. Struct. Ko 169 57 2016 10.1016/j.compstruc.2016.03.002 The MITC4+ shell element and its performance 

  40. Comput. Struct. Lee 223 2019 10.1016/j.compstruc.2019.07.005 The strain-smoothed MITC3+ shell element 

  41. Internat. J. Numer. Methods Engrg. Taylor 10 1211 1976 10.1002/nme.1620100602 A non-conforming element for stress analysis 

  42. Kohnke 1998 ANSYS Theory Reference: Release 5.5 

  43. Comput. Struct. Bathe 89 285 2011 10.1016/j.compstruc.2010.09.007 Measuring the convergence behavior of shell analysis schemes 

  44. Adv. Eng. Softw. Lee 41 712 2010 10.1016/j.advengsoft.2009.12.011 The quadratic MITC plate and MITC shell elements in plate bending 

  45. Comput. Struct. Ko 193 187 2017 10.1016/j.compstruc.2017.08.003 Performance of the MITC3+ and MITC4+ shell elements in widely-used benchmark problems 

  46. Cook 2007 Concepts and Applications of Finite Element Analysis 

  47. Comput. Sci. Eng. Walt 13 22 2011 10.1109/MCSE.2011.37 The NumPy array: a structure for efficient numerical computation 

  48. Comput. Methods Appl. Mech. Engrg. Yoon 281 106 2014 10.1016/j.cma.2014.07.023 Nonlinear performance of continuum mechanics based beam elements focusing on large twisting behaviors 

  49. Comput. Struct. Lee 138 12 2014 10.1016/j.compstruc.2014.02.005 The MITC3+ shell finite element and its performance 

  50. Comput. Struct. Ko 192 34 2017 10.1016/j.compstruc.2017.07.003 A new 4-node MITC element for analysis of two-dimensional solids and its formulation in a shell element 

  51. Comput. Struct. Kim 216 40 2019 10.1016/j.compstruc.2018.12.002 New enriched 3D solid finite elements: 8-node hexahedral, 6-node prismatic, and 5-node pyramidal elements 

  52. Comput. Struct. Jeon 146 91 2015 10.1016/j.compstruc.2014.09.004 The MITC3+ shell element in geometric nonlinear analysis 

  53. Comput. Struct. Kim 87 1451 2009 10.1016/j.compstruc.2009.05.002 A triangular six-node shell element 

  54. Internat. J. Numer. Methods Engrg. Bucalem 36 3729 1993 10.1002/nme.1620362109 Higher-order MITC general shell elements 

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