Our mission is to address the grand challenges in modeling and simulating fluids, solids, and manufacturing related problems through accurate, robust, and scalable numerical methods as well as machine learning and data-driven model order reduction techniques. Our research currently spans the following areas:

  • Machine learning (ML) and artificial intelligence (AI)-empowered surrogate modeling for complex systems
  • Probabilistic reduced order modeling for solid materials and extreme events 
  • Quantum algorithms for topology optimization and linear systems
  • Data-driven coarse-grained modeling of soft matter
  • Data-driven non-intrusive reduced order modeling for fluids, fluid-solid interactions, and multiphase flows
  • High-fidelity simulations (enabled by high-order accurate numerical methods) for fluid-solid interactions 
  • Multiphysics modeling and simulations in manufacturing, energy storage, and thermal sciences

High-Performance Computing Tools:

  • Parallel scalable in-house code for graph neural network-based modeling of complex fluids 
  • Parallel scalable in-house code for high-fidelity simulations of fluid-solid interactions based on adaptive generalized moving least square (GMLS)
  • Coarse-grained simulation methods implemented in LAMMPS