Material Point Method
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Related Articles from SNS
An implicit octree-based adaptive Material Point Method
Announce Type: new Abstract: The Material Point Method provides an effective approach for modelling the large deformations that often arise from contact interactions between rigid structures and surrounding continua. However, solving these problems requires accurate representation of the continuum-structure interface, which necessitates high resolution background mesh and material point discretisations. This requirement, combined with evolving continuum-structure interfaces and the fact that...
Unified sparse framework for large-scale material point method simulations
Announce Type: replace-cross Abstract: The material point method (MPM) is a hybrid particle-grid method widely used for simulating large deformation with history-dependent behavior. Standard MPM often relies on a dense background grid, which can be highly inefficient when material occupies a small fraction of the computational domain. Such sparsity is common in many large-scale problems, from geophysical mass flows over large terrain domains to visual-computing applications.
Unified sparse framework for large-scale material point method simulations
Announce Type: replace Abstract: The material point method (MPM) is a hybrid particle-grid method widely used for simulating large deformation with history-dependent behavior. Standard MPM often relies on a dense background grid, which can be highly inefficient when material occupies a small fraction of the computational domain. Such sparsity is common in many large-scale problems, from geophysical mass flows over large terrain domains to visual-computing applications.
MPMWorlds: Material-Point-Method Simulations for Inferring and Extrapolating Physical Dynamics
Announce Type: new Abstract: To study the ability to infer physical dynamics from videos and extrapolate them forward in time, we assemble a dataset of 2D Material Point Method (MPM) physical simulations covering rich physical phenomena such as deformable objects, fluids, kinetic objects, and emitters. We study code generation and video diffusion approaches on this dataset, identifying their strengths and weaknesses by varying the amount of physically relevant side information. The code...
OnlyDense: Reduced-Order Modeling for Lagrangian simulation
arXiv:2606.09065v1 Announce Type: new Abstract: In science and engineering, Lagrangian simulation methods such as Smooth Particle Hydrodynamics (SPH) or Material Point Method (MPM) are often employed to study the behavior of dynamic systems. However, these methods can be prohibitively computationally expensive, particularly when simulating multi-scale spatial or temporal phenomena, e.g., void growth and coalescence within macro-scale geometries, structural failure of spacecraft components...
ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning -- Additional Material
arXiv:2605.30225v2 Announce Type: replace Abstract: Clustering is an unsupervised technique for grouping data points by similarity. While explainability methods exist for supervised machine learning, they are not directly applicable to clustering, making it challenging to understand cluster assignments. This interpretability gap is particularly evident in the popular density-based method DBSCAN, which assigns points as inliers (cluster members in dense regions) or outliers (noise points in...
PhysAgent: Automating Physics-Based 4D Synthesis via Trajectory-Grounded Multi-Agent Feedback
Announce Type: new Abstract: Achieving fully automated, physically plausible 3D motion synthesis is a core objective in graphics and generative AI. However, configuring complex environmental force fields still relies entirely on manual expert intervention, creating a severe bottleneck for large-scale simulation data generation. Existing automated methods primarily focus on material optimization and exhibit severe modality gaps and technical flaws when applied to the vastly more complex force...
UniPixie: Unified and Probabilistic 3D Physics Learning via Flow Matching
arXiv:2606.05399v1 Announce Type: new Abstract: Existing feed-forward networks excel at predicting a single set of physical properties from visual appearance, but this point-estimate paradigm fundamentally fails to capture the real world's inherent physical ambiguity. We address this by reframing physics prediction as a task of learning a controllable, continuous distribution of material properties. We introduce UNIPIXIE, a framework trained to predict a continuous and parameterized path of...
AI Agent Guidelines for CS336 at Stanford
This file provides instructions for AI coding assistants (like ChatGPT, Claude Code, GitHub Copilot, Cursor, etc.) working with students in CS336. AI agents should function as teaching aids that help students learn through explanation, guidance, and feedback—not by completing assignments for them.
'We restore paintings to their former glory'
'We restore paintings to their former glory' Visiting stately homes or art galleries, the paintings offer a glimpse into the past; how people dressed, what a place looked like, what myths or fables inspired art. What also might strike you is, after centuries on display, how can some remain in such good condition, the colours still bright, the details clear and defined? For a vast swathe of paintings in venues across Yorkshire, the answers lies in a Sheffield studio, where old artwork is...