The broader fields that we work in are complex fluids and soft matter. Our mission is to address the grand challenges in modeling and simulating complex fluids and soft matter through accurate, robust, and scalable numerical methods, machine learning and data-driven model order reduction techniques. Our research currently spans the following areas.

Data-driven coarse-grained modeling of soft matter:

Soft matter, such as polymers and nanoparticles, composed of molecular and/or modular building blocks can provide the structural and chemical plenitude and tunability to yield paradigm-shifting multifunctional materials for broad applications. A grand challenge in designing multifunctional materials made from soft matter is to link the emergent functionality to the underlying multiscale structures. To address this challenge, atomistic simulations are computationally prohibitive. Thus, coarse-grained (CG) modeling is particularly attractive, which projects atomistic details onto a coarser representation and is well suited to bridge multiscale structure-function relationships in materials.

  • Preserve both structural and dynamic properties under coarse-graining 

“Implicit-solvent coarse-grained modeling for polymer solutions via Mori-Zwanzig formalism”, Soft Matter, 15, 7567-7582 (2019). Back cover

“Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved”, Soft Matter, 16, 8330-8344 (2020). Front cover

  • Transfer learning of memory kernels for achieving transferable coarse-graining of polymer dynamics

“Transfer Learning of Memory Kernels in Coarse-grained Modeling”, arXiv:2103.10578

  • Coarse-graining of non-equilibrium systems

“Data-driven Coarse-grained Modeling of Non-equilibrium Systems”, submitted.

Fluid-solid interactions:

Fluid-solid interactions are ubiquitous in complex fluids. Computer simulations can enhance fundamental understanding and predict before experimental realization on how fluid and solids interact with each other and exhibit rich behaviors under different conditions and external forces. However, simulating fluid-solid interactions is challenging because of moving fluid–solid interfaces of arbitrary geometries, intractable cost to resolve point singularities that govern lubrication effects, and deteriorated convergence in the presence of singularities. Our research in this area aims to tackle these challenges.

  • High-fidelity simulations enabled by high-order, spatially adaptive, meshless discretization methods
    • Generalized moving least square (GMLS) with h-refinement

Computer Methods in Applied Mechanics and Engineering, 355, 67-93 (2019)

    • Consistent, spatially adaptive smoothed particle hydrodynamics (SPH); implicit and incompressible SPH

Computer Methods in Applied Mechanics and Engineering, 347, 402-424 (2019); Computer Methods in Applied Mechanics and Engineering, 324, 278-299 (2017); Journal of Computational Physics 334, 125–144 (2017)

  • Data-driven non-intrusive reduced order modeling (ROM)

For large-scale applications and applications requiring multi-query loops (e.g., optimization and control), high-fidelity simulations that solve the full governing PDEs are too expensive. Therefore, the research in this area aims to establish accurate and efficient ROM for fluid-solid interactions, and more generally, for dynamic systems with freely moving boundaries/interfaces.

Computer Methods in Applied Mechanics and Engineering, 373, 113495 (2021)

High-Performance Computing Tools:

  • Coarse-grained simulation methods implemented in LAMMPS
  • Parallel scalable in-house code for adaptive GMLS