Droplet Simulations in Computer Graphics: Theories, Methods and Applications
November 24, 2024 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hossein Keshtkar, Nadine Aburumman
arXiv ID
2411.15880
Category
physics.flu-dyn
Cross-listed
cs.GR
Citations
0
Venue
arXiv.org
Last Checked
1 month ago
Abstract
Creating realistic droplet simulations and animations has long been a formidable challenge for researchers and developers due to the inherent complexity of fluid dynamics. Achieving lifelike droplet splash simulations while managing computational resources has often resulted in sacrifices compromising the realism of visualizations. Nevertheless, significant progress has been made in the past two decades, driven by advancements in particle-based methods such as Position-Based Dynamics (PBD) and Smoothed-Particle Hydrodynamics (SPH). These methods have enabled the simulation of droplet splash behaviour with increasing accuracy and reduced computational complexity. Integrating features like surface tensions, fluid incompressibility, and liquid-wall interactions has further enhanced the realism of the simulations. This paper provides an in-depth exploration of the theoretical foundations and methodologies employed in droplet simulations and how they have evolved over time. Accurate droplet interaction visualization holds immense potential across diverse applications, including gaming, animation, medical simulations, and engineering scenarios like 3D printing simulations.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ physics.flu-dyn
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Efficient collective swimming by harnessing vortices through deep reinforcement learning
R.I.P.
๐ป
Ghosted
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
R.I.P.
๐ป
Ghosted
Teaching the Incompressible Navier-Stokes Equations to Fast Neural Surrogate Models in 3D
R.I.P.
๐ป
Ghosted
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning
R.I.P.
๐ป
Ghosted
From Deep to Physics-Informed Learning of Turbulence: Diagnostics
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted