CS Events

PhD Defense Generation and Optimization of Physics-Based Graphics Simulations
Export Event Save iCal Send to Google Calendar Send to Yahoo Calendar Send to Outlook Live Send to Microsoft Outlook Save iCal Preserve formatting in description (only supported in some calendar applications) Close Wednesday, December 10, 2025, 12:00pm - 02:00pm

Speaker: Alon Flor 

Location : CBIM 22

Committee: 

Professor Mridul Aanjaneya

Professor Peng Zhang

Professor Richard Martin

Professor Christopher Batty (external)

Event Type: PhD Defense

Abstract: Simulations of physical phenomena are used in many fields, from the study of liquids and solids inmaterial science, to the exploration of diffusion phenomena, to creating animations and graphics, andmachine learning models. Such simulations are compute-intensive. Their usefulness is often limited bythe speed and accuracy that can be achieved, by memory limitations, and by the difficulty of coding.The research work presented in this dissertation addresses these limitations and offers improvements,touching on all three aspects of physical simulation: a direct use case, the speed of the simulation, andthe ease of writing the simulation.The work started with a direct use case for physics simulation: a detailed simulation of the spread ofCOVID-19 within moving crowds. It integrated an ecological model of diffusion and immunity tosimulate the disease spread, with a force model to calculate the crowd movement. The interactivesimulation was the first to model disease spread within the field of computer graphics.The model was insightful, however the simulations were slow and computationally expensive,particularly in 3D. This obstacle led to a second study, which investigated speeding up 3D simulationsvia memory access optimization. That study focused on the simulation of elastic solids. Usually,methods of optimizing simulations for speed make solver-specific or scenario-specific optimizations. Incontrast, the technique presented in this dissertation’s second study is a general acceleration methodthat can be used to speed up any simulation of elastic solids. This is accomplished by rearranging thedata of the elastic solid meshes to make the ordering of the vertices and faces cache-efficient. Therearranged data can then be used in any solver, resulting in a faster simulation run.The work done to optimize the simulations brought to the fore the complications inherent inprogramming efficient physical simulations. The programmer is required to have intricate knowledgeof the internal workings of the computer. The simulation code must be written in a low-level language(usually C++). The necessity of low-level programming results in bloated and repetitive code, withplenty of room for bugs to hide. The final part of this dissertation describes a solution to this hurdle forgrid-based fluid simulations: a domain-specific language called Spade. In Spade, users specify theparameters of a simulation in a high-level specification file, which is then automatically compiled intooptimized C++ code. The resulting simulations are based on the sparse grid representation, which hasrecently emerged as an indispensable component of fluid simulation pipelines due to its impact onmemory savings and performance. In Spade, the complexities of maximizing computational efficiency(through leveraging cache coherency and parallelization) are handled behind the scenes, allowing thedeveloper to focus on defining the grid, data, and the core algorithm.

Organization: 

Rutgers University

School of Arts and Sciences

Department of Computer Science

University of Waterloo (external)

Contact  Professor Mridul Aanjaneya

Zoom Link: https://rutgers.zoom.us/j/95208731346?pwd=wsaAQQ0ZRZisowMpTWA2y5YvfEbtJo.1