Natarajan Sukumar

N. Sukumar

Position Title
Professor

  • Civil and Environmental Engineering
He/Him/His
3159 Ghausi
Bio

Dr. Sukumar's research interests are in new computational methods development in solid mechanics and quantum mechanics. Sukumar's research group focuses on advancing modern finite element and meshfree Galerkin methods to simulate large deformation and fracture in solids and structures, and in devising enriched partition ­of ­unity Galerkin methods for large ­scale ab initio quantum­ mechanical materials calculations. These ef forts aim to enable predictive numerical simulations that can capture quasi­static and dynamic fracture processes in solid continua, and new computational real­ space methods that can be an efficient alternative to existing ab initio planewave (Fourier­ based) methods to accurately describe the electronic structure of matter.

Honors and Awards
  • 2016 The auspices of the Ministry of Human Resources Development (Government of India) Program on Global Initiative Academic Networks, GIAN
  • 2013 - Regional Editor of International Journal of Fracture
  • 2014 - Member of the Editorial Board of Finite Elements in Analysis and Design
  • 2014-2018 Member of the Executive Council of the USACM
Publications
  • A Wachspress-based transfinite formulation for exactly enforcing Dirichlet boundary conditions on convex polygonal domains in physics-informed neural networks
    Journal Article | Sukumar, N., Roy, R. Computational Mechanics, 1-30. 10.1007/s00466-026-02789-4
  • CutVEM: Conforming virtual element method on embedded domains with shape-agnostic element agglomeration
    Journal Article | Rangarajan, R., Sukumar, N. Computer Methods in Applied Mechanics and Engineering, 449, 118540. 10.1016/j.cma.2025.118540
  • Stress-hybrid virtual element method on ten-noded tetrahedral meshes for nearly-incompressible elastic solids and thin structures
    Journal Article | Chen, A., Bishop, J., Sukumar, N. Computer Methods in Applied Mechanics and Engineering, 461, 119284. 10.1016/j.cma.2026.119284
  • FO-PINN: A First-Order formulation for Physics-Informed Neural Networks
    Journal Article | Gladstone, R., Nabian, M., Sukumar, N., Srivastava, A., Meidani, H. Engineering Analysis with Boundary Elements, 174, 106161. 10.1016/j.enganabound.2025.106161
  • Variational formulation based on duality to solve partial differential equations: Use of B-splines and machine learning approximants
    Journal Article | Sukumar, N., Acharya, A. Computer Methods in Applied Mechanics and Engineering, 441, 117909. 10.1016/j.cma.2025.117909
  • See publications and more on Aggie Experts: https://experts.ucdavis.edu/expert/BqidDhS1

Tags