Granular matter is ubiquitous in nature and is present in diverse forms in important
engineering, industrial and natural processes. Particle-based computational modelling has …

Gas-solid fluidization technology has been commercialized in many industrial applications
since its implementation in the fluid catalytic cracking process in the early 1940s, however …

We propose a hybrid neural network (NN) and PDE approach for learning generalizable
PDE dynamics from motion observations. Many NN approaches learn an end-to-end model …

We extend the incremental potential contact (IPC) model for contacting elastodynamics to
resolve systems composed of codimensional DOFs in arbitrary combination. This enables a …

Regolith penetration for placing instruments into ground and regolith-sampling for re-entry or
in-situ analysis play an extremely critical role in searching for alien life and revealing the …

We present two new approaches for animating dynamic fracture involving large elastoplastic
deformation. In contrast to traditional mesh-based techniques, where sharp discontinuity is …

We propose a hybrid neural network and physics framework for reduced-order modeling of
elastoplasticity and fracture. State-of-the-art scientific computing models like the Material …

Harnessing the power of modern multi-GPU architectures, we present a massively parallel
simulation system based on the Material Point Method (MPM) for simulating physical …

Abstract A 3-D concurrent MPM-DEM (material point method-discrete element method)
scheme is proposed and developed in this paper to simulate soil-rock mixture slopes. The …

Aiming at modelling the mechanical behaviour of soil-rock mixtures accurately and
efficiently, a coupled MPM-DEM formulation combining the material point method (MPM) …