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Kun Wang

CNLS Postdoctoral Research Associate
CNLS, EES-17

Machine Learning in Geophysics

Kun Wang

Office: TA-03, Bldg 1690, Room 107
Mail Stop: B216
Phone: (505) 000-0000
Fax: (505) 665-2659

kunw@lanl.gov
home page

Research highlight
  • Convolutional auto-encoder and spectral analysis for predicting fault failure from seismic signals.
  • Physics-informed convolutional auto-encoder for fluid flow in nano-cale pores of shale formation.
  • Deep reinforcement learning for developing computational mechanics models.
  • Multiscale LBM-DEM-FEM coupling for fractured porous media simulations.
 Educational Background/Employment:
  • 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
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