In this paper, we introduce the Moving Least Squares Material Point Method (MLS-MPM).
MLS-MPM naturally leads to the formulation of Affine Particle-In-Cell (APIC)[Jiang et al …

Recent progress in deep learning is essentially based on a “big data for small tasks”
paradigm, under which massive amounts of data are used to train a classifier for a single …

Simulating the physical behaviors of deformable objects and fluids has been an important
topic in computer graphics. While the Lagrangian Finite Element Method (FEM) is widely …

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 notion of affordance that takes into account physical quantities generated
when the human body interacts with real-world objects, and introduce a learning framework …

Algorithm 5 Update Damage (Explicit Only) 1: for each particle, p, do 2://Compute geometric
resistance: Dn c 3: Dn c= dn p− l2 0∆ dn p 4://Compute driving force:(1− dn p) Dn p 5: Get un …

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 …

We present a new hybrid Lagrangian Material Point Method for simulating elastic objects
like hair, rubber, and soft tissues that utilizes a Lagrangian mesh for internal force …

Virtual cutting of deformable bodies has been an important and active research topic in
physically based modelling and simulation for more than a decade. A particular challenge in …

In this paper, we propose Energetically Consistent Inelasticity (ECI), a new formulation for
modeling and discretizing finite strain elastoplasticity/viscoelasticity in a way that is …