Physics-based fluid simulation in computer graphics: Survey, research trends, and challenges
Physics-based fluid simulation has played an increasingly important role in the computer
graphics community. Recent methods in this area have greatly improved the generation of …
graphics community. Recent methods in this area have greatly improved the generation of …
Efficient learning of mesh-based physical simulation with bi-stride multi-scale graph neural network
Learning the long-range interactions on large-scale mesh-based physical systems with flat
Graph Neural Networks (GNNs) and stacking Message Passings (MPs) is challenging due …
Graph Neural Networks (GNNs) and stacking Message Passings (MPs) is challenging due …
Bi-stride multi-scale graph neural network for mesh-based physical simulation
Learning physical systems on unstructured meshes by flat Graph neural networks (GNNs)
faces the challenge of modeling the long-range interactions due to the scaling complexity …
faces the challenge of modeling the long-range interactions due to the scaling complexity …
Mendnet: Restoration of fractured shapes using learned occupancy functions
We provide a novel approach to perform fully automated generation of restorations for
fractured shapes using learned implicit shape representations in the form of occupancy …
fractured shapes using learned implicit shape representations in the form of occupancy …
Unstructured moving least squares material point methods: a stable kernel approach with continuous gradient reconstruction on general unstructured tessellations
The material point method (MPM) is a hybrid Eulerian Lagrangian simulation technique for
solid mechanics with significant deformation. Structured background grids are commonly …
solid mechanics with significant deformation. Structured background grids are commonly …
Multi-scale graph neural network for physics-informed fluid simulation
Learning-based fluid simulation has proliferated due to its ability to replicate the dynamics
with substantial computational savings over traditional numerical solvers. To this end, graph …
with substantial computational savings over traditional numerical solvers. To this end, graph …
Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN
Learning the physical simulation on large-scale meshes with flat Graph Neural Networks
(GNNs) and stacking Message Passings (MPs) is challenging due to the scaling complexity …
(GNNs) and stacking Message Passings (MPs) is challenging due to the scaling complexity …
Material Point Methods on Unstructured Tessellations: A Stable Kernel Approach With Continuous Gradient Reconstruction
The Material Point Method (MPM) is a hybrid Eulerian-Lagrangian simulation technique for
solid mechanics with significant deformation. Structured background grids are commonly …
solid mechanics with significant deformation. Structured background grids are commonly …
Portable, Massively Parallel Implementation of a Material Point Method for Compressible Flows
PJ Baioni, T Benacchio, L Capone… - arXiv preprint arXiv …, 2024 - arxiv.org
The recent evolution of software and hardware technologies is leading to a renewed
computational interest in Particle-In-Cell (PIC) methods such as the Material Point Method …
computational interest in Particle-In-Cell (PIC) methods such as the Material Point Method …
GPUs based material point method for compressible flows
P Baioni, T Benacchio, L Capone… - The Material Point …, 2023 - re.public.polimi.it
Abstract Particle-In-Cell (PIC) methods such as the Material Point Method (MPM) can be cast
in formulations suitable to the requirements of data locality and fine-grained parallelism of …
in formulations suitable to the requirements of data locality and fine-grained parallelism of …