Smoothed Particle Hydrodynamics Techniques for the Physics Based Simulation of Fluids and Solids
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Abstract (arXiv)
Graphics research on Smoothed Particle Hydrodynamics (SPH) has produced fantastic visual results that are unique across the board of research communities concerned with SPH simulations. Generally, the SPH formalism serves as a spatial discretization technique, commonly used for the numerical simulation of continuum mechanical problems such as the simulation of fluids, highly viscous materials, and deformable solids. Recent advances in the field have made it possible to efficiently simulate massive scenes with highly complex boundary geometries on a single PC [Com16b, Com16a]. Moreover, novel techniques allow to robustly handle interactions among various materials [Com18,Com17]. As of today, graphics-inspired pressure solvers, neighborhood search algorithms, boundary formulations, and other contributions often serve as core components in commercial software for animation purposes [Nex17] as well as in computer-aided engineering software [FIF16]. This tutorial covers various aspects of SPH simulations. Governing equations for mechanical phenomena and their SPH discretizations are discussed. Concepts and implementations of core components such as neighborhood search algorithms, pressure solvers, and boundary handling techniques are presented. Implementation hints for the realization of SPH solvers for fluids, elastic solids, and rigid bodies are given. The tutorial combines the introduction of theoretical concepts with the presentation of actual implementations.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2020.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 3; 2025: 1; 2026: 2.
9 papers.
- 2024VideoPhy: Evaluating Physical Commonsense for Video Generationcited by 215
- 2022Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamicscited by 30
- 2023Fluid Dynamics Network: Topology-Agnostic 4D Reconstruction via Fluid Dynamics Priorscited by 7
- 2024JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Frameworkcited by 3
- 2025Scan, Materialize, Simulate: A Generalizable Framework for Physically Grounded Robot Planningcited by 2
- 2022Wavelet-based Loss for High-frequency Interface Dynamicscited by 1
- 2024A Pioneering Neural Network Method for Efficient and Robust Fluid Simulationcited by 1
- 2026Learning Mesh-Free Discrete Differential Operators with Self-Supervised Graph Neural Networkscited by 1
- 2026SWIM: Compact Environment Representation for Reinforcement Learning Human Swimmingcited by 0
- 2026SWIM: Compact Environment Representation for Reinforcement Learning Human Swimming29 May 2026
- 2026Learning Mesh-Free Discrete Differential Operators with Self-Supervised Graph Neural Networks25 Mar 2026
- 2025Scan, Materialize, Simulate: A Generalizable Framework for Physically Grounded Robot Planning20 May 2025
- 2024A Pioneering Neural Network Method for Efficient and Robust Fluid Simulation14 Dec 2024
- 2024VideoPhy: Evaluating Physical Commonsense for Video Generation5 Jun 2024
- 2024JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework7 Mar 2024
- 2023Fluid Dynamics Network: Topology-Agnostic 4D Reconstruction via Fluid Dynamics Priors17 Mar 2023
- 2022Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics12 Oct 2022
- 2022Wavelet-based Loss for High-frequency Interface Dynamics6 Sep 2022