Position based fluids

By Miles Macklin, Matthias Müller. 2013

500 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

Citations in the corpus

Divergence-free smoothed particle hydrodynamics (2015, methodology): “In contrast to non-iterative as well as iterative EOS solvers, the Pressure Poisson Equation (PPE) can be solved in order to project intermediate velocities onto a divergence-free state.”
Visual Grounding of Learned Physical Models (2020, methodology): “Instead, our model assumes a particle-based intermediate representation (Macklin & M¨uller, 2013), allowing us to model interactions between objects of different materials, including rigid bodies, de-formable objects, and ﬂuids.”
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids (2018, methodology): “For ﬂuid simulation, one has to enforce density and incompressibility, which can be effectively achieved by only considering a small neighborhood for each particle (Macklin & M¨uller, 2013).”
Unified particle physics for real-time applications (2014, methodology): “In contrast to [Macklin and Müller 2013] we clamp the density constraint to be non-negative (unilateral), so that it only acts to separate particles, C(x1, ...,xn) = ρi ρ0 − 1 ≤ 0, (26) we then use the model of [Akinci et al. 2013a] to handle cohesion and surface tension effects.”
Pre-computed Liquid Spaces with Generative Neural Networks and Optical Flow (2017, background): “Particle-based liquid simulations are also highly interesting for fast simulations [Solenthaler and Pajarola 2009; Macklin and Müller 2013] Likewise, many recent techniques focus on novel representations to allow for fast simulations, e.g. to represent highly detailed surfaces with meshes…”
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments (2023, methodology): “the throughput than DEDO while using PBD solver [42].”
Data-driven fluid simulations using regression forests (2015, methodology): “Originally we intended to use the random scene generator of PBF simulations [Macklin and Mueller 2013] directly in the training pipeline without any necessity to store the intermediate data, however due to a rare divergence”
Lagrangian Fluid Simulation with Continuous Convolutions (2020, methodology): “…in our context is the work of Wang et al. (2018), who used continuous convolutions to compute the scene ﬂow between two point clouds, and the aforementioned work of Schenck & Fox (2018), who used convolutions to implement a differentiable version of position-based ﬂuids (Macklin & M¨uller, 2013).”
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics (2018, unknown): “Recently, [26] proposed SPNets for differentiable simulation of position-based fluids [27].”
Projective dynamics (2014, unknown): “This problem becomes even more accentuated if we want to extend our approach to ﬂuid simulation similar to [Macklin and M¨uller 2013] where neighborhood relations always change.”