Publications

For an up-to-date list of publications, please see this Google scholar page.

Selected Journal Articles

  1. Deng, S., Mora, C., Apelian, D., & Bostanabad, R. (2022). Data-Driven Calibration of Multi-Fidelity Multiscale Fracture Models via Latent Map Gaussian Process. Journal of Mechanical Design, 1-15.
  2. Deng, S., Soderhjelm, C., Apelian, D., & Bostanabad, R. (2022). Reduced-order multiscale modeling of plastic deformations in 3D alloys with spatially varying porosity by deflated clustering analysis. Computational Mechanics, 1-32.
  3. Eweis-Labolle, J., Oune, N., & Bostanabad, R. (2022). Data Fusion with Latent Map Gaussian Processes. Journal of Mechanical Design, 1-41.
  4. Wang, H., Planas, R., Chandramowlishwaran, A., & Bostanabad, R. (2022). Mosaic flows: A transferable deep learning framework for solving PDEs on unseen domains. Computer Methods in Applied Mechanics and Engineering, 389, 114424.
  5. Chen, W., Iyer, A., & Bostanabad, R. (2022). Data Centric Design: A New Approach to Design of Microstructural Material Systems. Engineering, 10, 89-98.
  6. Oune, N., & Bostanabad, R. (2021). Latent map Gaussian processes for mixed variable metamodeling. Computer Methods in Applied Mechanics and Engineering, 387, 114128.
  7. Planas Casadevall, R., Oune, N., & Bostanabad, R. (2021). Evolutionary gaussian processes. Journal of Mechanical Design, 143(11), 111703.
  8. Suh, Y., Bostanabad, R., & Won, Y. (2021). Deep learning predicts boiling heat transfer. Scientific Reports, 11(1), 5622. doi:10.1038/s41598-021-85150-4.
  9. Bostanabad, R. (2020). Reconstruction of 3D Microstructures from 2D Images via Transfer Learning. Computer-Aided Design, 128, 102906. doi:10.1016/j.cad.2020.102906.
  10. Mozaffar, M., Bostanabad, R., Chen, W., Ehmann, K., Cao, J., & Bessa, M. A. (2019). Deep learning predicts path-dependent plasticity. Proceedings of the National Academy of Sciences, 116(52), 26414-26420.
  11. Bostanabad, R., Y.-C. Chan, L. Wang, P. Zhu and W. Chen (2019). Globally Approximate Gaussian Processes for Big Data with Application to Data-Driven Metamaterials Design. Journal of Mechanical Design, 141, 1-11.
  12. Zhang, W.*, Bostanabad, R.*, Liang, B., Su, X., Zeng, D., Bessa, M. A., Wang, Y., Chen, W. & Cao, J. (2019). A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling. Composites Science and Technology, 170, 15-24.
  13. Bostanabad, R., Liang, B., Gao, J., Liu, W. K., Cao, J., Zeng, D., Su, X., Xu, H., Li, Y. & Chen, W. (2018). Uncertainty quantification in multiscale simulation of woven fiber composites. Computer Methods in Applied Mechanics and Engineering, 338, 506-532.
  14. Bostanabad, R., Zhang, Y., Li, X., Kearney, T., Brinson, L. C., Apley, D. W., Liu, W. K. & Chen, W. (2018). Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques. Progress in Materials Science, 95, 1-41.
  15. Bostanabad, R., Kearney, T., Tao, S., Apley, D. W. & Chen, W. (2018). Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516.
  16. Hassaninia, I.*, Bostanabad, R.*, Chen, W. & Mohseni, H. (2017). Characterization of the Optical Properties of Turbid Media by Supervised Learning of Scattering Patterns. Scientific Reports, 7, 15259.
  17. Bessa, M. A., Bostanabad, R., Liu, Z., Hu, A., Apley, D. W., Brinson, C., Chen, W. & Liu, Wing K. (2017). A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality. Computer Methods in Applied Mechanics and Engineering, 320, 633-667.
  18. Bostanabad, R., Chen, W. & Apley, D. W. (2016). Characterization and reconstruction of 3D stochastic microstructures via supervised learning. Journal of Microscopy, 264, 282-297.
  19. Bostanabad, R., Bui, A. T., Xie, W., Apley, D. W. & Chen, W. (2016). Stochastic microstructure characterization and reconstruction via supervised learning. Acta Materialia, 103, 89-102.

Selected Refereed Conference Papers

  1. Planas, R., Oune, N., & Bostanabad, R. (2020, August). Extrapolation With Gaussian Random Processes and Evolutionary Programming. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 84003, p. V11AT11A004). American Society of Mechanical Engineers.
  2. Bostanabad, R., Y.-C. Chan, L. Wang, P. Zhu and W. Chen (2019). Gaussian process emulation for big data in data-driven metamaterials design. Proceedings of the ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conferences.
  3. Tao, S., Shintani, K., Bostanabad, R., Chan, Y.-C., Yang, G., Meingast, H. & Chen, W. (2017). Enhanced Gaussian Process Metamodeling and Collaborative Optimization for Vehicle Suspension Design Optimization. In: ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers.