• Ph.D. (2019) Computational Geomechanics, Columbia University, New York, USA
• M.S. (2015) Mechanical Engineering, Columbia University, New York, USA
• Dipl. Ing. (2013) Mechanical Engineering, University of Technology of Troyes, Troyes, France
• Employment:
  • 2015-2019 Research Assistant, Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, MY.

• Professional Training:
  • 2019-present Postdoctoral Associate at Los Alamos National Laboratory, Los Alamos, NM.

Research Interests:

• Multi-scale and multi-physics modeling of geological systems
• Physics-informed data-driven computational geomechanics.
• Reinforcement learning for material constitutive modeling.

Selected Recent Publications:

Google Scholar: Kun Wang
1. K Wang, Y Chen, M Mehana, N Lubbers, KC Bennett, QJ Kang, HS Viswanathan, TC Germann, A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media, Journal of Computational Physics. under review, (2020).
2. K Wang, KC Bennett, PA Johnson, Estimating fault slow slips from seismic signals via convolutional auto-encoder and spectral analysis, IEEE Transactions on Geoscience and Remote Sensing. under review, (2020).
3. A Fuchs, Y Heider, K Wang, WC Sun, M Kaliske, DNN2: a hyperparameter reinforcement learning game for self-design neural network elasto-plastic constitutive laws, Computational Mechanics. under review, (2020).
4. K Wang, WC Sun, Q Du, A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks, Computer Methods in Applied Mechanics and Engineering. 373, (2020). Link
5. Y Heider, K Wang, WC Sun, SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials, Computer Methods in Applied Mechanics and Engineering. 363, (2020). Link