All Issue

2024 Vol.29, Issue 3 Preview Page

Research Article

30 September 2024. pp. 109-122
Abstract
References
1

2019, Kim, B., Azevedo, V. C., Thuerey, N., Kim, T., Gross, M. and Solenthaler, B., "Deep Fluids: A Generative Network for Parameterized Fluid Simulations," Comput. Graph. Forum, Vol.38, No.2, pp.59-70.

10.1111/cgf.13619
2

2021, Markidis, S., "The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?" Front. Big Data, Vol.4.

10.3389/fdata.2021.669097
3

2022, Li, J., Zhang, M., Tay, C.M.J., Liu, N., Cui, Y., Chew, S.C. and Khoo, B.C., "Low-Reynolds-number airfoil design optimization using deep-learning-based tailored airfoil modes," Aerosp. Sci. Technol., Vol.121, 107309.

10.1016/j.ast.2021.107309
4

2024, Yang, S., Kim, H., Hong, Y., Yee, K., Maulik, R. and Kang, N., "Data-driven physics-informed neural networks: A digital twin perspective," Comput. Methods Appl. Mech. Eng., Vol.428, 117075.

10.1016/j.cma.2024.117075
5

2016, Ribeiro, M.T., Singh, S. and Guestrin, C., "Why Should I Trust You?: Explaining the Predictions of Any Classifier," Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA.

6

2017, Lundberg, S.M. and Lee, S.I., "A unified approach to interpreting model predictions," Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA.

7

2019, Schlegel, U., Arnout, H., El-Assady, M., Oelke, D. and Keim, D.A., "Towards A Rigorous Evaluation Of XAI Methods On Time Series," 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp.4197-4201.

10.1109/ICCVW.2019.00516
8

2021, Yoo, S. and Kang, N., "Explainable artificial intelligence for manufacturing cost estimation and machining feature visualization," Expert Syst. Appl., Vol.183, pp.115430.

10.1016/j.eswa.2021.115430
9

1997, Hochreiter, S. and Schmidhuber, J. "Long short-term memory," Neural Comput., Vol.9, No.8, pp.1735-1780.

10.1162/neco.1997.9.8.1735
10

2014, Sutskever, I., Vinyals, O. and Le, Q.V., "Sequence to sequence learning with neural networks," Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, Montreal, Canada.

11

2014, Bahdanau, D., Cho, K. and Bengio, Y., "Neural Machine Translation by Jointly Learning to Align and Translate," ArXiv, abs/1409.0473.

12

2015, Luong, T., Pham, H. and Manning, C.D., "Effective Approaches to Attention-based Neural Machine Translation," Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing Lisbon, Portugal.

10.18653/v1/D15-1166
13

2014, Graves, A., Wayne, G. and Danihelka, I., "Neural turing machines," ArXiv, abs/1410.5401.

14

2017, Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., "Attention is all you need," Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA.

15

2008, Kulfan, B.M., "Universal parametric geometry representation method," J. Aircr., Vol.45, No.1, pp.142-158.

10.2514/1.29958
16

2022, Hong, Y., Lee, D., Yee, K. and Park, S.H., "Enhanced High-Order Scheme for High-Resolution Rotorcraft Flowfield Analysis," AIAA J., Vol.60, No.1, pp.144-159.

10.2514/1.J060803
17

2004, Park, S.H. and Kwon, J.H., "Implementation of kw turbulence models in an implicit multigrid method," AIAA J., Vol.42, No.7, pp.1348-1357.

10.2514/1.2461
18

1981, Pulliam, T.H. and Chaussee, D.S., "A diagonal form of an implicit approximate-factorization algorithm," J. Comput. Phys., Vol.39, No.2, pp.347-363.

10.1016/0021-9991(81)90156-X
19

2003, Aupoix, B. and Spalart, P.R. "Extensions of the Spalart-Allmaras Turbulence Model to Account for Wall Roughness," Int. J. Heat Fluid Flow, Vol.24, No.4, pp.454-462.

10.1016/S0142-727X(03)00043-2
20

2017, Jameson, A. "Origins and further development of the Jameson-Schmidt-Turkel scheme," AIAA J., Vol.55, No.5, pp.1487-1510.

10.2514/1.J055493
21

2022, Krishna, S., Han, T., Gu, A., Pombra, J., Jabbari, S., Wu, S. and Lakkaraju, H., "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective," ArXiv, abs/2202.01602.

22

1979, Cook, P.H., McDonald, M.A. and Firmin, M.C.P., "Aerofoil rae 2822-pressure distributions, and boundary layer and wake measurements. experimental data base for computer program assessment," AGARD report ar 138: 47.

23

2018, Petsiuk, V., Das, A. and Saenko, K., "RISE: Randomized Input Sampling for Explanation of Black-box Models," ArXiv, abs/1806.07421.

24

2018, Alvarez-Melis, D. and Jaakkola, T.S., "On the robustness of interpretability methods," ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden.

Information
  • Publisher :Korean Society for Computational Fluids Engineering
  • Publisher(Ko) :한국전산유체공학회
  • Journal Title :Journal of Computational Fluids Engineering
  • Journal Title(Ko) :한국전산유체공학회지
  • Volume : 29
  • No :3
  • Pages :109-122
  • Received Date : 2024-07-04
  • Revised Date : 2024-08-29
  • Accepted Date : 2024-09-20