Fluid Simulation for Computer Graphics

By Robert Bridson. 2008

550 citations recorded by the source corpus.

Animating fluids like water, smoke, and fire using physics-based simulation is increasingly important in visual effects, in particular in movies, like The Day After Tomorrow, and in computer games. This book provides a practical introduction to fluid simulation for graphics. The focus is on animating fully three-dimensional incompressible flow, from understanding the math and the algorithms to the actual implementation.

Subfields: Computer Graphics and Computer-Aided Design

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In Computer Graphics and Computer-Aided Design

Citations in the corpus

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting (2015, methodology): “ROVER calculates the optical ﬂow of consecutive radar maps using the algorithm in [5] and performs semi-Lagrangian advection [4] on the ﬂow ﬁeld, which is assumed to be still, to accomplish the prediction.”
Reasoning About Liquids via Closed-Loop Simulation (2017, methodology): “They are commonly used to model ﬂuid ﬂow in areas such as mechanical and aerospace engineering [9], and to model liquid surfaces in computer graphics [2, 6, 17].”
Data-driven synthesis of smoke flows with CNN-based feature descriptors (2017, unknown): “Furthermore, procedural turbulence methods to increase apparent resolutions are popular extensions [Kim et al. 2008; Narain et al. 2008; Schechter and Bridson 2008].”
Generating Liquid Simulations with Deformation-aware Neural Networks (2017, methodology): “As training data we generate sets of implicit surfaces from liquid simulations with the FLIP method [6].”
The material point method for simulating continuum materials (2016, methodology): “While FLIP has been known in graphics community as a useful liquid simulation method for a while [Zhu and Bridson, 2005; Bridson, 2008; Ando and Tsuruno, 2011], MPM is only introduced and studied recently.”
Accelerating Eulerian Fluid Simulation With Convolutional Networks (2016, background): “For readers unfamiliar with ﬂuid mechanics and it’s associated prerequisite topics (multi-variable calculus, ﬁnite-difference meth-ods, etc), we highly recommend (Bridson, 2008) as an introductory reference to this material.”
The scope and limits of simulation in automated reasoning (2016, background): “richer physics than a pendulum, such as fluid dynamics, these kinds of problems can arise in much more complex forms [6, 23].”
Divergence-free smoothed particle hydrodynamics (2015, methodology): “For a general survey we refer to the work of Bridson [2008] while the recent state of the art report of Ihmsen et al.”
The affine particle-in-cell method (2015, background): “Incompressibility is enforced in the standard way [Bridson 2008].”
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, background): “ approaches, where the computational grid is ﬁxed in space, and physical properties advect through the deformation ﬂow. A typical example is the Eulerian simulation of free surface incompressible ﬂow [391,392]. Eulerian methods are more error-prone and require delicate treatment when dealing with de”
A Dimension‐reduced Pressure Solver for Liquid Simulations (2015, methodology): “Eulerian solvers based on ﬁnite difference discretizations are commonly used in the graphics community, and a good overview can be found in the book by Bridson [Bri08].”