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About Applications of Deep Learning Operator Networks for Design and Optimization of Advanced Materials and Processes Cover

About Applications of Deep Learning Operator Networks for Design and Optimization of Advanced Materials and Processes

By: Seid Korić and  Diab W. Abueidda  
Open Access
|Jan 2023

References

  1. [1] E. Fatehi, H. Yazdani Sarvestani, B. Ashrafi, A. H. Akbarzadeh: Accelerated design of architectured ceramics with tunable thermal resistance via a hybrid machine learning and finite element approach, Materials & Design, vol. 210, 110056, 202110.1016/j.matdes.2021.110056
  2. [2] H. T. Kollmann, D. W. Abueidda, S. Koric, E. Guleryuz, N. A. Sobh: Deep learning for topology optimization of 2D metamaterials, Materials & Design, vol. 196, 110056, 202110.1016/j.matdes.2020.109098
  3. [3] Q. Zhu, Z. Liu, J. Yan: Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks, Computational Mechanics, vol. 67, pp. 619-635, 202110.1007/s00466-020-01952-9
  4. [4] G. X. Gu, C. Chen, D. J. Richmond, M. J. Buehler: Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment, Materials Horizons vol. 5.5, pp. 939-945, 201810.1039/C8MH00653A
  5. [5] S. Shahane, E. Guleryuz, D. W. Abueidda, A. Lee, J. Liu, X. Yu, R. Chiu, S. Koric, N. R. Aluru, P. M. Ferreira: Surrogate neural network model for sensitivity analysis and uncertainty quantification of the mechanical behaviour in the optical lens-barrel assembly, Computers & Structures, vol. 270, 106843, 202210.1016/j.compstruc.2022.106843
  6. [6] D. W. Abueidda, Q. Lu, S. Koric: Meshless physics-informed deep learning method for three-dimensional solid mechanics, International Journal for Numerical Methods in Engineering, vol. 122(23), pp. 7182-7201, 202110.1002/nme.6828
  7. [7] L. Lu., P. Jin., G. Pang., Z. Zhang, G. E. Karniadakis, Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators, Nat. Mach. Intell., vol. 3, pp. 218–229, 202110.1038/s42256-021-00302-5
  8. [8] S. Wang, H. Wang, P. Perdikaris, Learning the solution operator of parametric partial differential equations with physics-informed DeepONets, Science Advances, vol. 7(40), pp. 1-9, 202110.1126/sciadv.abi8605848092034586842
  9. [9] S. Pattanayak: Pro Deep Learning with Tensorflow: a Mathematical Approach to Advanced Artificial Intelligence in python, Apress, Springer New York, 201710.1007/978-1-4842-3096-1_1
  10. [10] GRoT Linear and Nonlinear Finite Element Method solver, https://github.com/tutajrobert/grot
  11. [11] O. C. Zienkiewicz and R. L. Taylor., 2000. The Finite Element Method 5th ed., Oxford Butterworth-Heinemann, Portsmouth, NH, 2000
  12. [12] S. Koric, B. G Thomas: Efficient thermo-mechanical model for solidification processes, International Journal for Numerical Methods in Engineering, vol. 66, pp. 1955–1989, 200610.1002/nme.1614
  13. [13] J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, Q. Zhang: JAX: Composable transformations of Python+NumPy programs (2018).
  14. [14] ABAQUS/Standard User’s Manual, Version 2019. Providence, RI: Dassault Systèmes Simulia Corp, 2019
DOI: https://doi.org/10.2478/bhee-2022-0006 | Journal eISSN: 2566-3151 | Journal ISSN: 2566-3143
Language: English
Page range: 1 - 6
Submitted on: Sep 6, 2022
Accepted on: Sep 26, 2022
Published on: Jan 14, 2023
Published by: Bosnia and Herzegovina National Committee CIGRÉ
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2023 Seid Korić, Diab W. Abueidda, published by Bosnia and Herzegovina National Committee CIGRÉ
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.