Our PhD graduate Kun Wang will join ExxonMobil Research and Engineering Company as Computational Physicist

Congratulations to Dr. Kun Wang who has been selected from around 200 applicants by ExxonMobil! He will join  Computational Physics Section at EMRE’s Corporate Strategic Research Laboratories as a computational physicist to develop computational methods aimed at solving large-scale physical problems pertaining to the energy industry, with focus in the areas of flow in porous media, multi-scale phenomena, PDE-constrained optimization and uncertainty quantification.

Kun has a distinguished career at both Columbia University and Los Alamos National Laboratory. His recent work published in Nature Communication and PNAS has been featured at Economist. During his time at Columbia, he has published 12 papers with the research group, as listed below. 
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Published Work:

1. K. Wang, W.C. Sun, A semi-implicit discrete-continuum coupling method for porous media based on the effective stress principle at finite strain, Computer Methods in Applied Mechanics and Engineering,  doi:10.1016/j.cma.2016.02.020, 2016. [DRAFT]
2. K. Wang, W.C. Sun, Anisotropy of a tensorial Bishop's coefficient for wetted granular materials, Journal of Engineering Mechanics, doi:10.1061/(ASCE)EM.1943-7889.0001005, 2015. [DRAFT] [Bibtex]
3. K. Wang, W.C. Sun, S. Salager, S. Na, G. Khaddour, Identifying material parameters for a micro-polar plasticity model via X-ray micro-CT images: lessons learned from the curve-fitting exercises, accepted, International Journal of Multiscale Computational Engineering, 2016. [DRAFT]
4. K. Wang, W.C. Sun, A unified variational eigen-erosion framework for interacting fractures and compaction bands in brittle porous media, doi:10.1016/j.cma.2017.01.017, Computer Methods in Applied Mechanics and Engineering, 2017. [DRAFT]
5. K. Wang, W.C. Sun,   A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning​, Computer Methods in Applied Mechanics and Engineering, 334(1):337-380, doi:10.1016/j.cma.2018.01.036, ​2018. 
6. R. Gupta, S. Salager, K. Wang, W.C. Sun, Open-source support toward validating and falsifying discrete mechanics models using synthetic granular materials Part I: Experimental tests with particles manufactured by a 3D printer, Acta Geotechnica, doi:10.1007/s11440-018-0703-0, 2018. 
7. K. Wang, W.C. Sun, An updated Lagrangian LBM-DEM-FEM coupling model for dual-permeability porous media with embedded discontinuities, Computer Methods in Applied Mechanics and Engineering, 344:276-305, doi:10.1016/j.cma.2018.09.034, 2019. [PDF][Bibtex]
8. K. Wang, W.C. Sun, Meta-modeling game for deriving theory-consistent, micro-structure-based traction-separation laws via deep reinforcement learning, Computer Methods in Applied Mechanics and Engineering, accepted, 346:216-241,  doi:10.1016/j.cma.2018.11.026, 2019. [PDF][Bibtex]
9. K. Wang, W.C. Sun, Q. Du, A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation, Computational Mechanics, special issue for Data-Driven Modeling and Simulations: Theory, Methods and Applications, doi:,10.1007/s00466-019-01723-1, 2019. 
10. Y. Heider, K. Wang, W.C. Sun, SO(3)-invariance of graph-based deep neural network for anisotropic elastoplastic materials, Computer Methods in Applied Mechanics and Engineering, doi:10.1016/j.cma.2020.112875, 2019.
11. K. Wang, W.C. Sun, Q. Du, A non-cooperative meta-modeling game for automated third-party training, validating, and falsifying constitutive laws with adversarial attacks, Computer Methods in Applied Mechanics and Engineering, doi:10.1016/j.cma.2020.113514, 2020. [arxiv][Video]
12. A. Fuchs, Y. Heider, K. Wang, W.C. Sun, M. Kaliske, DNN2: A hyper-parameter reinforcement learning game for self-design1neural network elasto-plastic constitutive laws, Computer and Structures, accepted, 2021.

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