Unified particle physics for real-time applications

By Miles Macklin, Matthias Müller, Nuttapong Chentanez, Tae-Yong Kim. 2014

550 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, AI for Physical Sciences

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Citations in the corpus

Learning to Simulate Complex Physics with Graph Networks (2020, methodology): “(2018)’s BOXBATH, which simulates a container of water and a cube floating inside, all represented as particles, using the PBD engine FleX (Macklin et al., 2014).”
Flexible Neural Representation for Physics Prediction (2018, methodology): “All training data for the below experiments was generated via a custom interactive particle-based environment based on the FleX physics engine [31] in Unity3D.”
Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow (2018, methodology): “Beyond these primarily grid-based techniques, smoothed particle hydrodynamics (SPH) are a popular Lagrangian alternative [Macklin et al. 2014; Müller et al. 2003].”
6-DOF Grasping for Target-driven Object Manipulation in Clutter (2019, unknown): “Physical interactions are simulated using FleX [43].”
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation (2019, methodology): “Training Data for Grasping To generate reference sets of successful grasps, we use the physics simulation FleX [18], which provides realistic simulation of grasps for arbitrary object shapes.”
Pre-computed Liquid Spaces with Generative Neural Networks and Optical Flow (2017, background): “While it is possible to perform real-time simulations with enough computing power at hand [Macklin et al. 2014], many practical applications cannot rely on a dedicated GPU at their full disposal.”
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics (2018, unknown): ““Flex” refers to [31], the real-time simulator from NVIDIA.”
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids (2018, background): “Particle-based system is widely used in physical simulation due to its flexibility in modeling various types of objects (Macklin et al., 2014).”
Eulerian solid-fluid coupling (2016, methodology): “To date, most attempts at such a solver have been Lagrangian [Solenthaler et al. 2007; Akinci et al. 2013; Clausen et al. 2013; Macklin et al. 2014], because SPH-like [Macklin and Müller 2013; Ihmsen et al. 2014] and FLIP-like [Jiang et al. 2015] methods can be used to make the fluid…”
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes (2021, methodology): “We also compare different variations of our method and of our data by executing a large number of predicted grasps in the FleX physics simulator [37].”
Lagrangian Fluid Simulation with Continuous Convolutions (2020, methodology): “Below, we adopt the position-based ﬂuids (PBF) method (Macklin & M ¨ uller, 2013; Macklin et al., 2014), which likewise is based on SPH, but reformulates the updates as constraints on the positions.”
A moving least squares material point method with displacement discontinuity and two-way rigid body coupling (2018, methodology): “The approach is further extended in [Macklin et al. 2014] to allow solid- uid coupling through density constraints.”
Visual Grounding of Learned Physical Models (2020, methodology): “We use NVIDIA FleX (Macklin et al., 2014), a particlebased physics engine to generate all data for training and testing.”
Kubric: A scalable dataset generator (2022, methodology): “ThreeDWorld [34] is an excellent example for such an engine with a flexible Python API, comprehensive export capabilities to a Unity3D based rasterization engine, the NVidia Flex [61] physics simulator and even sound generation via PyImpact [96].”
Modeling, learning, perception, and control methods for deformable object manipulation (2021, background): “Ropes (13, 14, 15) Fabrics (16, 17) Simple to implement Lacking physical interpretability Sponges (17, 18) Rubber spheres (18 where k j denotes the stiffness of the j th constraint C j ( x 1 , x 2 , …, x n ) = 0, j = 1,2, ….”
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments (2023, methodology): “On the other hand, frameworks for deformable bodies [4], [8] mainly employ Bullet [18] or FleX [19], which use particle-based dynamics for soft bodies and cloth simulation.”