DOCSLIB.ORG

Project Description CSE 788.14: Physically Based Simulation Instructor: Huamin Wang

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Project Description CSE 788.14: Physically Based Simulation Instructor: Huamin Wang Project Description CSE 788.14: Physically Based Simulation Instructor: Huamin Wang 1 Policies The goal of this project is to put what you learned from the classroom into practice, and hopefully you will find these techniques useful in your future research. Specifically, you will implement a simulation technique described by one or a few papers. The project will contribute to 60 percent of your final grade and it is due on December 5th to 8th. It can be either an individual project, or a group of two. If you plan to work on a group project, the total load should be twice as the individual project and each of you should list your tasks in the final project report. You will give three short presentations on your project (8 mins each): • Proposal presentation (Mid October). This presentation should include the following con- tents: (1) a short video clip showing the real natural phenomena that you plan to animate; (2) several clips showing state-of-the-art animation results in this topic; (3) briefly description on papers or techniques you plan to use; (4) any technical difficulties; (5) the timeline. • Midterm presentation (early November). It should cover: (1) papers you have read and progress you have made; (2) any technical difficulties and you plan to solve them; (3) any early results you have achieved so far. • Final presentation (the end of the quarter). This presentation will summarize what you did for the project. It should contain (1) a summary on the problem you have solved; (2) your results and a comparison between your results and the results from the paper; (3) what improvements could be made to this work in the future. In addition, you should submit the source code, animation results, and a final project report to me by email after the final presentation. The project can be done together with any existing research project you are currently working on, as long as you can demonstrate sufficient additional work in simulation. You are free to discuss the project with others, however, if you are using others' code, you must notify it in presentations and the project report. 2 Requirements and Tips Depending on your project topic, the project result can either be a real-time simulation demo, or an offline simulated video. If it is a real-time demo, you may use either OpenGL or GPU shader 1 to do real-time rendering, and you must also provide some basic user interactions in simulation. Please check course demos as references1. If you decide to do offline simulation, you can output your simulation data from your simulator into a file sequence, and then load them into any rendering program for offline rendering, such as Maya, POV-Ray, or even your own renderer. Finally, you can use video software (such as virtualdub and Adobe Premiere) to make rendered images into a video. 3 Examples A few possible individual projects are listed below for you to choose from. You are also free to choose other projects that are not provided in this list, with the equivalent amount of work. 3.1 Rigid Body collisions When hundreds of rigid bodies fall at the same place, they will collide and stack upon each other (shown in Figure 1a). Rigid body simulation is not difficult, however, simulating their collisions and contacts needs some work. This project needs to implement: • Both vertex-face and edge-edge collision detection; • Collision Impulses when two bodies collide; • Resting contact, in order to avoid oscillation artifacts; • Bonus: At least one acceleration algorithm to speed up the simulation; Suggested papers: [2, 9] 3.2 Mass-Spring Cloth Simulator Cloth can be animated by using a mass-spring system (Figure 1b). In this system, particles are connected by springs with some resting lengths and the simulation is an extension of the single particle case we discussed in class. Depending on your cloth mesh resolution, the simulation can either be real-time (typically less than 1K particles), or off-line. You will need to: • Use the implicit Euler method to formulate a linear system; • Solve the system using a matrix solver (such as the conjugate gradient method); • Detect collisions between cloth and rigid objects, and handle them as constraints; • Bonus: Detect and handle cloth-cloth collisions as well using the continuous approach [3] Suggested papers: [5, 3, 17] 1http://www.cse.ohio-state.edu/ whmin/courses/cse788-2011-fall/cse788-2011-fall.html 2 3.3 Elastic Body By removing the rotational component out of the strain tensor, The co-rotational FEM method makes the Cauchy strain and the resulting implicit system more accurate. It is an efficient way to simulate elastic bodies. Figure 1c shows that a tube is bent using the co-rotational method. This project can also be made off-line if you will use high-resolution tetrahedral meshes. To implement this method, you need to: • Calculate strain and stress tensors over a tetrahedral mesh using the finite element method; • Remove the rotationa matrix, then formulate a linear system using the implicit method; • Solve the system using a matrix solver (such as the conjugate gradient method); • Bonus: User interactions and collisions Suggested papers: [13, 15] for basic FEM/FVM, [12, 8] for the co-rotational FEM. 3.4 2D Smoke 2D smoke animation can be simulated by solving the Navier-Stokes equations using the finite dif- ferencing method, as Figure 1d shows. In order to demonstrate the smoke effect, the grid resolution is suggested to be at least 100-by-100 and the simulation can be done offline. The following steps are required in order to implement such a simulator: • Calculate a set of velocity updates according to the Navier-Stokes equations; • Solve a matrix system to make the velocity field divergence free • Evolve the smoke density field over time • Bonus: Make it in 3D. Suggested papers: [14, 7]. For those who plan to work on 3D water simulation as a group project, you will need to implement the level set method, the marching cube method that maintains the signed distance function, and the marching cube method to obtain a surface mesh representation. Refer to [6] and their online course notes [4] for more details. 3.5 3D Smoothed Particle Hydrodynamics (SPH) Water can also be simulated as particles in 3D. An example is shown in 1c (although you don't have to implement an adaptive SPH algorithm). To implement the standard SPH technique, you need to: • build an acceleration scheme to efficiently find neighboring particles; • formulate particle dynamics based on SPH; • handle particle interactions with solid boundaries; • Bonus: Make it fast. Suggested papers: [11, 4]. 3 References [1] Bart Adams, Mark Pauly, Richard Keiser, and Leonidas J. Guibas. Adaptively sampled particle fluids. ACM Transactions on Graphics (SIGGRAPH 2007), 26, July 2007. [2] David Baraff. Physically based modeling: Siggraph 2001 course notes. In ACM SIGGRAPH 2001 Courses, SIGGRAPH 2001, 2001. [3] R. Bridson, S. Marino, and R. Fedkiw. Simulation of clothing with folds and wrinkles. In Proc. of the ACM SIGGRAPH/Eurographics symposium on Computer animation, pages 28{36, 2003. [4] Robert Bridson, Ronald Fedkiw, and Matthias Muller-Fischer. Fluid simulation: Siggraph 2006 course notes. In ACM SIGGRAPH 2006 Courses, SIGGRAPH 2006, pages 1{87, 2006. [5] Kwang-Jin Choi and Hyeong-Seok Ko. Stable but responsive cloth. ACM Transactions on Graphics (SIGGRAPH 2002), 21:604{611, July 2002. [6] Douglas Enright, Stephen Marschner, and Ronald Fedkiw. Animation and rendering of complex water surfaces. ACM Transactions on Graphics (SIGGRAPH 2002), 21:736{744, July 2002. [7] Ronald Fedkiw, Jos Stam, and Henrik Wann Jensen. Visual simulation of smoke. In Pro- ceedings of the 28th annual conference on Computer graphics and interactive techniques, ACM Transactions on Graphics (SIGGRAPH 2001), pages 15{22, 2001. [8] Joachim Georgii and Rdiger Westermann. Corotated finite elements made fast and stable. In VRIPHYS'08, pages 11{19, 2008. [9] Eran Guendelman, Robert Bridson, and Ronald Fedkiw. Nonconvex rigid bodies with stacking. ACM Transactions on Graphics (SIGGRAPH 2003), 22:871{878, July 2003. [10] Shu-Wei Hsu and John Keyser. Piles of objects. ACM Transactions on Graphics (SIGGRAPH Asia 2010), 29:155:1{155:6, December 2010. [11] Matthias M¨uller,David Charypar, and Markus Gross. Particle-based fluid simulation for interactive applications. In Proceedings of SCA 2003, SCA '03, pages 154{159, 2003. [12] Matthias M¨uller,Julie Dorsey, Leonard McMillan, Robert Jagnow, and Barbara Cutler. Stable real-time deformations. In Proceedings of SCA 2002, SCA '02, pages 49{54, 2002. [13] James F. O'Brien and Jessica K. Hodgins. Graphical modeling and animation of brittle fracture. In Proc. of SIGGRAPH 99, pages 137{146, 1999. [14] Jos Stam. Stable fluids. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, SIGGRAPH '99, pages 121{128, 1999. [15] J. Teran, S. Blemker, V. Ng Thow Hing, and R. Fedkiw. Finite volume methods for the simulation of skeletal muscle. In Proc. of SCA 2003, pages 68{74, 2003. [16] Huamin Wang, James O'Brien, and Ravi Ramamoorthi. Multi-resolution isotropic strain lim- iting. ACM Transactions on Graphics (SIGGRAPH 98), 29(6):156:1{156:10, December 2010. [17] Chris Wojtan, Peter J. Mucha, and Greg Turk. Keyframe control of complex particle systems using the adjoint method. In Proc. of SCA 2006, pages 15{23, 2006. 4 (a) A pile of rigid bunnies, from [10]. (b) A square cloth falling onto a rigid sphere, (c) A tube bent under user-applied forces (real-time), from [16]. from [12]. (d) A 2D smoke simulation example (e) An adaptive SPH result, from [1] Figure 1: Image examples from previous work.
Load more

Recommended publications
  • Robust High-Resolution Cloth Using Parallelism, History-Based Collisions and Accurate Friction Andrew Selle, Jonathan Su, Geoffrey Irving, Ronald Fedkiw
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 1 Robust High-Resolution Cloth Using Parallelism, History-Based Collisions and Accurate Friction Andrew Selle, Jonathan Su, Geoffrey Irving, Ronald Fedkiw Abstract—In this paper we simulate high resolution cloth consisting of up to 2 million triangles which allows us to achieve highly detailed folds and wrinkles. Since the level of detail is also influenced by object collision and self collision, we propose a more accurate model for cloth-object friction. We also propose a robust history-based repulsion/collision framework where repulsions are treated accurately and efficiently on a per time step basis. Distributed memory parallelism is used for both time evolution and collisions and we specifically address Gauss-Seidel ordering of repulsion/collision response. This algorithm is demonstrated by several high-resolution and high-fidelity simulations. Index Terms—Computer Graphics, Geometric algorithms, Physically based modelling, Cloth, Collision response, Friction ! 1 INTRODUCTION LOTH simulation is pervasive in film, e.g. the untan- C gling strategies for Monsters Inc. [1], the collision and stiction methods for Terminator 3 and Harry Potter [2], and the wrinkle system for Shrek 2 [3]. Cloth sim- ulation also promises to have significant future impact on the clothing industry [4] (see also [5]). While, some researchers have focused on real-time simulation for computer games or non-hero characters [6] that have Bridson [10] Method Our Method lower quality requirements, our focus is on hero char- (45,000 triangles) (1,700,000 triangles) acters, online shopping, and related applications that need photorealistic cloth and clothing. Achieving such Fig.
    [Show full text]
  • Real-Time Fluid Simulation on GPU
    An Improved Study of Real-Time Fluid Simulation on GPU Enhua Wu1, 2, Youquan Liu1, Xuehui Liu1 1Laboratory of Computer Science, Institute of Software Chinese Academy of Sciences, Beijing, China 2Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macao, China email: [email protected], [email protected], [email protected] http://lcs.ios.ac.cn/~lyq/demos/gpgpu/gpgpu.htm Abstract Taking advantage of the parallelism and programmability of GPU, we solve the fluid dynamics problem completely on GPU. Different from previous methods, the whole computation is accelerated in our method by packing the scalar and vector variables into four channels of texels. In order to be adaptive to the arbitrary boundary conditions, we group the grid nodes into different types according to their positions relative to obstacles and search the node that determines the value of the current node. Then we compute the texture coordinates offsets according to the type of the boundary condition of each node to determine the corresponding variables and achieve the interaction of flows with obstacles set freely by users. The test results prove the efficiency of our method and exhibit the potential of GPU for general-purpose computations. KEY WORDS: Graphics Hardware, GPU, Programmability, NSEs, Fluid Simulation, Real-Time Introduction Real-time fluid simulation is a hot topic not only in computer graphics research field for the special effects in movies and video games, but also in engineering applications field. However, the computation of Navier-Stokes equations (NSEs) used to describe the motion of fluids is really hard to achieve real-time.
    [Show full text]
  • A Novel Algorithm for Incompressible Flow Using Only a Coarse Grid Projection
    A Novel Algorithm for Incompressible Flow Using Only a Coarse Grid Projection Michael Lentine∗ Wen Zheng Ronald Fedkiw Stanford University Stanford University Stanford University Industrial Light + Magic Industrial Light + Magic Figure 1: High resolution smoke and water. (Left) Smoke flowing above a sphere on a 512 1024 512 grid. (Middle) Water pouring into a box on a 512 512 512 grid. (Right) Smoke flowing around a sphere on a 512 1024× 512 grid.× × × × × Abstract noise (see [Stam and Fiume 1993; Lamorlette and Foster 2002; Ras- mussen et al. 2003]) and curl noise (see [Bridson et al. 2007]) can be Large scale fluid simulation can be difficult using existing tech- used to enhance the visual fidelity of fluid simulations by coupling niques due to the high computational cost of using large grids. We the noise to the incompressible Navier-Stokes equations producing present a novel technique for simulating detailed fluids quickly. Our a more detailed flow. Alternatively, [Kim et al. 2008] and [Narain technique coarsens the Eulerian fluid grid during the pressure solve, et al. 2008] determine where to add noise using information from allowing for a fast implicit update but still maintaining the resolu- the existing simulation and then add it as a postprocess which al- tion obtained with a large grid. This allows our simulations to run at lows them to add noise where it is best suited. Other techniques a fraction of the cost of existing techniques while still providing the such as [Schechter and Bridson 2008] both determine where to add fine scale structure and details obtained with a full projection.
    [Show full text]
  • TAMAR T. SHINAR CURRICULUM VITAE Computer Science And
    TAMAR T. SHINAR CURRICULUM VITAE Computer Science and Engineering phone: (951) 827-5015 University of California, Riverside email: [email protected] Winston Chung Hall, Room 351 https://www.cs.ucr.edu/ shinar Riverside, CA 92521 ~ RESEARCH INTERESTS Physics-based animation, fluid-structure interaction, computational biology. EDUCATION Sept 2003 - Jun 2008 Ph.D., Scientific Computing and Computational Mathematics Stanford University Thesis: Simulation of coupled rigid and deformable solids and multiphase fluids Research Advisor: Prof. Ronald Fedkiw Sept 1995 - May 1998 B.S., Mathematics, minor Computer Science University of Illinois at Urbana-Champaign magna cum laude with Highest Distinction in Mathematics Sept 1994 - May 1995 Iowa State University coursework in Physics, Chemistry, and Mathematics concurrent with senior year of high school ACADEMIC APPOINTMENTS Jul 2020 - present Associate Professor Jul 2011 - Jun 2020 Assistant Professor Computer Science and Engineering University of California, Riverside Sept 2008 - Jun 2011 Postdoctoral Fellow Courant Institute of Mathematical Sciences New York University Research Advisor: Prof. Michael Shelley HONORS AND AWARDS 2014 - 2017, 2011 - 2014 Amrik Singh Poonian Term Chair in Computer Science and Engineering Sept 1995 - Jun 1996 University of Illinois Jobst Scholar Spring 1995 Frank Miller Scholarship PROFESSIONAL AFFILIATIONS 2006 - present Association for Computing Machinery (ACM) 2009 - 2012 American Physical Society (APS) 2009 - 2011 Genetics Society of America (GSA) PROFESSIONAL ACTIVITIES
    [Show full text]
  • Skinning a Parameterization of Three-Dimensional Space for Neural Network Cloth
    Skinning a Parameterization of Three-Dimensional Space for Neural Network Cloth Jane Wu1 Zhenglin Geng1 Hui Zhou2,y Ronald Fedkiw1,3 1Stanford University 2JD.com 3Epic Games 1{janehwu,zhenglin,rfedkiw}@stanford.edu [email protected] Abstract We present a novel learning framework for cloth deformation by embedding virtual cloth into a tetrahedral mesh that parametrizes the volumetric region of air surround- ing the underlying body. In order to maintain this volumetric parameterization during character animation, the tetrahedral mesh is constrained to follow the body surface as it deforms. We embed the cloth mesh vertices into this parameterization of three-dimensional space in order to automatically capture much of the nonlinear deformation due to both joint rotations and collisions. We then train a convolutional neural network to recover ground truth deformation by learning cloth embedding offsets for each skeletal pose. Our experiments show significant improvement over learning cloth offsets from body surface parameterizations, both quantitatively and visually, with prior state of the art having a mean error five standard deviations higher than ours. Moreover, our results demonstrate the efficacy of a general learn- ing paradigm where high-frequency details can be embedded into low-frequency parameterizations. 1 Introduction Cloth is particularly challenging for neural networks to model due to the complex physical processes that govern how cloth deforms. In physical simulation, cloth deformation is typically modeled via a partial differential equation that is discretized with finite element models ranging in complexity from variational energy formulations to basic masses and springs, see e.g. [7, 13, 14, 19, 8, 51].
    [Show full text]
  • An Efficient Linear Octree-Based Grid Toward Magnetic Fluid Stimulation
    An Eicient Linear Octree-Based Grid Toward Magnetic Fluid Simulation Sean Flynn Parris Egbert Brigham Young University Brigham Young University ABSTRACT including animation, video games, science, and engi- An Eulerian approach to uid ow provides an ecient, neering. Over the past few decades, computer graph- stable paradigm for realistic uid simulation. However, ics research has had great success in creating convinc- its reliance on a xed-resolution grid is not ideal for ing uid phenomena including ocean waves, smoke simulations that exhibit both large and small-scale uid plumes, water splashing into containers, etc. By tak- phenomena. A coarse grid can eciently capture large- ing a physically-based approach, the burden on artists scale eects like ocean waves, but lacks the resolution needing to hand-animate complex uid behavior has needed for small-scale phenomena like droplets. On the been alleviated. Additionally, by providing controllable other hand, a ne grid can capture these small-scale ef- parameters, modern uid simulation techniques pro- fects, but is inecient for large-scale phenomena. Mag- vide artists some control over simulations. However, netic uid, or ferrouid, illustrates this problem with because physically-based uid simulation is computa- its very ne detail centered about a magnet and lack of tionally intensive, ecient algorithms and data struc- detail elsewhere. Our new uid simulation technique tures are crucial to support the ever-increasing demand builds upon previous octree-based methods by simulat- for new types of simulations. Depending on the art ing on a custom linear octree-based grid structure. A direction of a simulation, the simulator’s congura- linear octree is stored contiguously in memory rather tion details can vary widely.
    [Show full text]
  • Math Goes to the Movies 14 April 2010
    Math goes to the movies 14 April 2010 numerical methods and algorithms that can be implemented on computers to obtain approximate solutions. The kinds of approximations needed to, for example, simulate a firestorm, were in the past computationally intractable. With faster computing equipment and more-efficient architectures, such simulations are feasible today---and they drive many of the most spectacular feats in the visual effects industry. Another motivation for development in this area of All hairs are simulated in this hybrid research is the need to provide a high level of volumetric/geometric collision algorithm developed by controllability in the outcome of a simulation in two of the authors, Aleka McAdams and Joseph Teran order to fulfill the artistic vision of scenes. To this (together with additional co-authors). Credit: Copyright end, special effects simulation tools, while Disney Enterprises, Inc. physically based, must be able to be dynamically controlled in an intuitive manner in order to ensure believability and the quality of the effect. Whether it's an exploding fireball in "Star Wars: Episode 3", a swirling maelstrom in "Pirates of the Caribbean: At World's End", or beguiling rats turning out gourmet food in "Ratatouille", computer- generated effects have opened a whole new world of enchantment in cinema. All such effects are ultimately grounded in mathematics, which provides a critical translation from the physical world to computer simulations. The use of mathematics in cinematic special effects is described in the article "Crashing Waves, Penny's hair from BOLT is simulated using a clumping Awesome Explosions, Turbulent Smoke, and technique.
    [Show full text]
  • Alternating Convlstm: Learning Force Propagation with Alternate State Updates
    Alternating ConvLSTM: Learning Force Propagation with Alternate State Updates Congyue Deng Tai-Jiang Mu Shi-Min Hu Department of Mathematical Science Department of Computer Science and Technology Tsinghua University Tsinghua University [email protected] {taijiang, shimin}@tsinghua.edu.cn Abstract Data-driven simulation is an important step-forward in computational physics when traditional numerical methods meet their limits. Learning-based simulators have been widely studied in past years; however, most previous works view simulation as a general spatial-temporal prediction problem and take little physical guidance in designing their neural network architectures. In this paper, we introduce the alternating convolutional Long Short-Term Memory (Alt-ConvLSTM) that models the force propagation mechanisms in a deformable object with near-uniform mate- rial properties. Specifically, we propose an accumulation state, and let the network update its cell state and the accumulation state alternately.We demonstrate how this novel scheme imitates the alternate updates of the first and second-order terms in the forward Euler method of numerical PDE solvers. Benefiting from this, our net- work only requires a small number of parameters, independent of the number of the simulated particles, and also retains the essential features in ConvLSTM, making it naturally applicable to sequential data with spatial inputs and outputs. We validate our Alt-ConvLSTM on human soft tissue simulation with thousands of particles and consistent body pose changes. Experimental results show that Alt-ConvLSTM efficiently models the material kinetic features and greatly outperforms vanilla ConvLSTM with only the single state update. 1 Introduction Physical simulation plays an important role in various fields such as computer animation [1, 2, 3, 4], mechanical engineering [5], and robotics [6, 7, 8, 9, 10, 11].
    [Show full text]
  • Reference List for 15-464 / 15-664 March 10, 2021
    Reference List for 15-464 / 15-664 March 10, 2021 We started by looking at the following paper. If you really want to get a spring mass cloth simulation right, my favorite reference is this one. Features to look into include (1) how collisions (including self collisions) are handled by applying impulses to the cloth particles so that these collisions are never allowed to happen and (2) how impulses are similarly applied for “strain limiting” so that the cloth never stretches beyond a desired amount, and (3) how friction is handled. Bridson R, Fedkiw R, Anderson J. Robust treatment of collisions, contact and friction for cloth animation. ACM Transactions on Graphics (ToG). 2002 Jul 1;21(3):594-603. http://dl.acm.org/citation.cfm?id=566623 One persistent problem with spring mass systems is that it can be difficult to set parameters for realistic appearance and to illustrate the different properties of different types of cloth. These two papers attempt to set parameters by optimizing to fit measurements taken on actual cloth swatches. Wang H, O'Brien JF, Ramamoorthi R. Data-driven elastic models for cloth: modeling and measurement. InACM Transactions on Graphics (TOG) 2011 Aug 7 (Vol. 30, No. 4, p. 71). ACM. http://graphics.berkeley.edu/papers/Wang-DDE-2011-08/ Bhat, Kiran S., Christopher D. Twigg, Jessica K. Hodgins, Pradeep K. Khosla, Zoran Popović, and Steven M. Seitz. "Estimating cloth simulation parameters from video." In Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation, pp. 37-51. Eurographics Association, 2003. http://graphics.cs.cmu.edu/projects/clothparameters/ This paper addresses perceptual issues, trying to understand how simulation parameters might map to perception of cloth properties.
    [Show full text]
  • Craig Schroeder Math Sciences Building 6310 Los Angeles, CA 90095 Curriculum Vitae [email protected] Research Interests
    520 Portola Plaza Craig Schroeder Math Sciences Building 6310 Los Angeles, CA 90095 Curriculum Vitae [email protected] http://hydra.math.ucla.edu/~craig Research Interests My research interests include computational fluid dynamics, solid mechanics, fluid- structure interaction, physically-based simulation for computer graphics, mathe- matical modelling, and scientific computing. Education q Stanford University, Stanford, CA June 2011 Ph.D. in Computer Science Advisor: Ronald Fedkiw } Ph.D. Thesis: “Coupled Simulation of Deformable Solids, Rigid Bodies, and } Fluids with Surface Tension” q Drexel University, Philadelphia, PA June 2006 M.S. in Computer Science Advisor: Ali Shokoufandeh } B.S. in Computer Science } B.S. in Mathematics } Summa Cum Laude; Cumulative G.P.A. 3.97 } Masters Thesis: “Metric Tree Weight Adjustment and Infinite Complete Binary } Trees As Groups” Research Experience q Postdoctoral Scholar Advisor: Joseph Teran July 2011 to Present University of California Los Angeles, Department of Mathematics q Graduate Research Assistant Advisor: Ronald Fedkiw October 2006 to June 2011 Stanford University, Department of Computer Science q Research Intern Advisor: John Anderson June 2007 to June 2011 Pixar Animation Studios q Guest Researcher June 2006 to September 2006 Center for Computing Sciences, Bowie, MD q Guest Researcher Advisor: Ana Ivelisse Aviles June 2005 to September 2005 National Institute of Standards and Technology, Gaithersburg, MD q Undergraduate Research Assistant Advisor: William Regli June 2002 to June 2005 Drexel University, Department of Computer Science Publications [1] Klár, G., Gast, T., Pradhana, A., Fu, C., Schroeder, C., Jiang, C., Teran, J. “Drucker- Prager Elastoplasticity for Sand Animation,” SIGGRAPH 2016, ACM Transactions on Graphics (SIGGRAPH 2016).
    [Show full text]
  • Visual Simulation of Smoke
    Visual Simulation of Smoke Ý Þ Ronald Fedkiw £ Jos Stam Henrik Wann Jensen Stanford University Aliaswavefront Stanford University Abstract the animation of gases such as smoke. We propose a model which is stable, rapid and doesn’t suffer from excessive numerical dis- In this paper, we propose a new approach to numerical smoke sipation. This allows us to produce animations of complex rolling simulation for computer graphics applications. The method pro- smoke even on relatively coarse grids (as compared to the ones used posed here exploits physics unique to smoke in order to design a in CFD). numerical method that is both fast and efficient on the relatively coarse grids traditionally used in computer graphics applications (as compared to the much finer grids used in the computational 1.1 Previous Work fluid dynamics literature). We use the inviscid Euler equations in The modeling of smoke and other gaseous phenomena has received our model, since they are usually more appropriate for gas mod- a lot of attention from the computer graphics community over the eling and less computationally intensive than the viscous Navier- last two decades. Early models focused on a particular phenomenon Stokes equations used by others. In addition, we introduce a physi- and animated the smoke’s density directly without modeling its ve- cally consistent vorticity confinement term to model the small scale locity [10, 15, 5, 16]. Additional detail was added using solid tex- rolling features characteristic of smoke that are absent on most tures whose parameters were animated over time. Subsequently, coarse grid simulations. Our model also correctly handles the inter- random velocity fields based on a Kolmogoroff spectrum were used action of smoke with moving objects.
    [Show full text]
  • Interview with Dr Stanley Osher
    Asia Pacific Mathematics Newsletter /ŶƚĞƌǀŝĞǁǁŝƚŚƌ^ƚĂŶůĞLJKƐŚĞƌ Rochelle Kronzek technology. Fourier analysis is a universal tool in applied mathematics, and due in a large measure to Meyer’s work, wavelet theory has become the new name for Fourier analysis. The IMU posted this notice during the ICM 2014 in Seoul in honoring Dr. Osher: “Stanley Osher is awarded the Gauss Prize for his influ- ential contributions to several fields in applied mathe- matics, and his far-ranging inventions have changed our conception of physical, perceptual, and mathematical concepts, giving us new tools to apprehend the world. Dr. Osher has made influential contributions in a broad variety of fields in applied mathematics. These include high resolution shock capturing methods for hyperbolic equations, level set methods, PDE based methods in computer vision and image processing, and The Carl Friederich Gauss Prizewas premiered in 2006 optimisation. His numerical analysis contributions, and is awarded every four years at the International including the Engquist–Osher scheme, TVD schemes, Conference of Mathematics. Presented jointly by the entropy conditions, ENO and WENO schemes and International Mathematics Union and the German numerical schemes for Hamilton–Jacobi type equations Mathematical Society, Stanley Osher became just the have revolutionised the field. His level set contributions third individual to be honored. include new level set calculus, novel numerical tech- The Gauss prize, which many now consider the most niques, fluids and materials modelling, variational important award in applied mathematics, was estab- approaches, high co-dimension motion analysis, lished to give recognition to mathematicians who have geometric optics, and the computation of discontinuous impacted the world outside of their field through contri- solutions to Hamilton–Jacobi equations; level set methods butions to the business world, technology and everyday have been extremely influential in computer vision, image life.
    [Show full text]