We present a novel wavelet method for the simulation of fluids at high spatial resolution. The algorithm enables large- and small-scale detail to be edited separately, allowing high-resolution detail to be added as a post-processing step. Instead of solving the Navier-Stokes equations over a highly refined mesh, we use the wavelet decomposition of a low-resolution simulation to determine the location and energy characteristics of missing high-frequency components. We then synthesize these missing components using a novel incompressible turbulence function, and provide a method to maintain the temporal coherence of the resulting structures. There is no linear system to solve, so the method parallelizes trivially and requires only a few auxiliary arrays. The method guarantees that the new frequencies will not interfere with existing frequencies, allowing animators to set up a low resolution simulation quickly and later add details without changing the overall fluid motion.

We propose an efficient scheme for evaluating nonlinear subspace forces (and Jacobians) associated with subspace deformations. The core problem we address is efficient integration of the subspace force density over the 3D spatial domain. Similar to Gaussian quadrature schemes that efficiently integrate functions that lie in particular polynomial subspaces, we propose cubature schemes (multi-dimensional quadrature) optimized for efficient integration of force densities associated with particular subspace deformations, particular materials, and particular geometric domains. We support generic subspace deformation kinematics, and nonlinear hyperelastic materials. For an r-dimensional deformation subspace with O(r) cubature points, our method is able to evaluate subspace forces at O(r^2) cost. We also describe composite cubature rules for runtime error estimation. Results are provided for various subspace deformation models, several hyperelastic materials (St.Venant-Kirchhoff, Mooney-Rivlin, Arruda-Boyce), and multimodal (graphics, haptics, sound) applications. We show dramatically better efficiency than traditional Monte Carlo integration.

We perform a detailed flop and bandwidth analysis of Jos Stam’s Stable Fluids algorithm on the CPU, GPU, and Cell. In all three cases, we find that the algorithm is bandwidth bound, with the cores sitting idle up to 96% of the time. Knowing this, we propose two modifications to accelerate the algorithm. First, a Mehrstellen discretization for the pressure solver which reduces the running time of the solver by a third. Second, a static caching scheme that eliminates roughly 99% of the random lookups in the advection stage. We observe a 2x speedup in the advection stage using this scheme. Both modifications apply equally well to all three architectures.

Recent efforts to visually capture the phenomena of boiling have proposed monolithic approaches that extend the basic techniques underlying existing fluid solvers. In this work, we show that if we instead treat boiling as a separate computational module to be loosely coupled to an existing solver, a very easy to implement, highly efficient algorithm can be designed that produces excellent visual results, even on coarse (64³) grids. The algorithm is also highly SIMD-amenable, allowing the boiling computation to be farmed out to a GPU or Playstation 3 Cell processor. Our algorithm takes less than 100 lines of commented, readable C++, and can be integrated into an existing particle level set fluid solver with virtually no modifications. A serial implementation consumes between 3-5% of the overall running time, and a preliminary SIMD implementation shows that a 64³ simulation runs at 130 FPS, making the computational cost of the module totally negligible.

We present a technique for synthesizing spatially and temporally varying textures on continuous flows using image or video input, guided by the physical characteristics of the fluid stream itself. This approach enables the generation of realistic textures on the fluid that correspond to the local flow behavior, creating the appearance of complex surface effects, such as foam and small bubbles. Our technique requires only a simple specification of texture behavior, and automatically generates and tracks the features and texture over time in a temporally coherent manner. Based on this framework, we also introduce a technique to perform feature-guided video synthesis. We demonstrate our algorithm on several simulated and recorded natural phenomena, including river streams and lava flows. We also show how our methodology can be extended beyond realistic appearance synthesis to more general scenarios, such as temperature-guided synthesis of complex surface phenomena over a liquid during boiling.

Turing first theorized that many biological patterns arise through the processes of reaction and diffusion. Subsequently, reaction-diffusion systems have been studied in many fields, including computer graphics. We first show that for visual simulation purposes, reaction-diffusion equations can be made unconditionally stable using a variety of straightforward methods. Second, we propose an anisotropy embedding that significantly expands the space of possible patterns that can be generated. Third, we show that by adding an advection term, the simulation can be coupled to a fluid simulation to produce visually appealing flows. Fourth, we couple fast marching methods to our anisotropy embedding to create a painting interface to the simulation. Unconditional stability to maintained throughout, and our system runs at interactive rates. Finally, we show that on the Cell processor, it is possible to implement reaction-diffusion on top of an existing fluid solver with no significant performance impact.

We present a fast method for simulating, animating, and rendering lightning using adaptive grids. The "dielectric breakdown model" is an elegant algorithm for electrical pattern formation that we extend to enable animation of lightning. The simulation can be slow, particularly in 3D, because it involves solving a large Poisson problem. Losasso et al. recently proposed an octree data structure for simulating water and smoke, and we show that this discretization can be applied to the problem of lightning simulation as well. However, implementing the incomplete Cholesky conjugate gradient (ICCG) solver for this problem can be daunting, so we provide an extensive discussion of implementation issues. ICCG solvers can usually be accelerated using "Eisenstat's trick," but the trick cannot be directly applied to the adaptive case. Fortunately, we show that an "almost incomplete Cholesky" factorization can be computed so that Eisenstat's trick can still be used. We then present a fast rendering method based on convolution that is competitive with Monte Carlo ray tracing but orders of magnitude faster, and we also show how to further improve the visual results using jittering.

(also SIGGRAPH 2005 Sketch A fast fractal growth algorithm)

We present a novel technique for synthesizing textures over dynamically changing fluid surfaces. We use both image textures as well as bump maps as example inputs. Image textures can enhance rendering of the fluid by imparting novel realistic appearance to it, whereas bump maps enable the generation of complex micro-structures on the surface of the fluid that may be very difficult to synthesize using simulation. To generate temporally coherent textures over a fluid sequence, we transport texture information, i.e. color and local orientation, between fluid free surfaces from one time step to the next. This is accomplished by extending the texture information from the first fluid surface to the 3D fluid domain, advecting this information within the fluid domain along the fluid velocity field for one time step, and interpolating it back onto the second surface -- this operation, in part, uses a novel vector advection technique for transporting orientation vectors. We then refine the transported texture by performing texture synthesis over the second surface using our `surface texture optimization algorithm, which keeps the synthesized texture visually similar to the input texture and temporally coherent with the transported one. We demonstrate our novel algorithm for texture synthesis on dynamically evolving fluid surfaces in several challenging scenarios.

Large, 3D ice formations such as icicles exhibit a high degree of geometric and optical complexity. Modeling these features by hand can be a daunting task, so we present a novel physically-based algorithm for simulating this phenomenon. Solidification is usually posed as a so-called `Stefan problem', but the problem in its classic form is inappropriate for simulating the ice typically found in a winter scene. We instead use the `thin-film' variant of the Stefan problem to derive velocity equations for a level set simulation. However, due to the scales involved in the problem, even an adaptive grid level set solver is still insufficient to track the tip of an icicle. Therefore, we derive an analytical solution for the icicle tip and use it to correct the level set simulation. The results appear to be in agreement with experimental data. We also present a physically-based technique for modeling ripples along the ice surface that alleviates the need to explicitly track small-scale geometry. To our knowledge, our approach is the most complete model available, and produces complex visual phenomena that no previous method has been able to capture.

We present a physically-based method for animating and rendering lightning and other electric arcs. For the simulation, we present the dielectric breakdown model, an elegant formulation of electrical pattern formation. We then extend the model to animate a sustained, "dancing" electrical arc, by using a simplified Helmholtz equation for propagating electromagnetic waves. For rendering, we use a convolution kernel to produce results competitive with Monte Carlo ray tracing. Lastly, we present user parameters for manipulation of the simulation patterns.

We present a novel algorithm that simulates ice formation. Motivated by the physical process of ice growth, we develop a novel hybrid algorithm by synthesizing three techniques: diffusion limited aggregation, phase field methods, and stable fluid solvers. Each technique maps to one of the three stages of solidification. The visual realism of the resulting algorithm appears to surpass that of each technique alone, particularly in animations of freezing. In addition, we present a faster, simplified phase field method, as well as a unified parameterization that enables artistic manipulation of the simulation. We illustrate the results on arbitrary 3D surfaces.

(also SIGGRAPH 2004 Sketch A physically based model of Ice)

The beautiful, branching structure of ice is one of the most striking visual phenomena of the winter landscape. Yet there is little study about modeling this effect in computer graphics. In this paper, we present a novel approach for visual simulation of ice growth. We use a numerical simulation technique from computational physics, the "phase field method," and modify it to allow aesthetic manipulation of ice crystal growth. We present acceleration techniques to achieve interactive simulation performance, as well as a novel geometric sharpening algorithm that removes some of the smoothing artifacts from the implicit representations. We have successfully applied this approach to generate ice crystal growth on 3D object surfaces in several scenes.

The Lévy Dragon is a well-known fractal introduced by Paul Lévy in 1938. It is a connected subset of the plane with interior (in fact it tiles the plane) but the interior is disconnected. Although the dragon has a fractal boundary of dimension 1.934007…, we show that each component of the interior has a polygonal boundary (with perhaps infinitely many edges) of finite length. There are infinitely many components, but we conjecture that they are all similar to one of sixteen different shapes. We show pictures of these shapes and some of the ways they interweave when two smaller dragons combine to make a larger dragon. We explain how we used the computer as a kind of "microscope" to reveal this structure. More pictures and programs are available on the web site http://www.mathlab.cornell.edu/~twk6/.