Published: 2014-10-30
ISBN: 978-91-7519-212-3
ISSN: 1650-3686 (print), 1650-3740 (online)
In this paper a novel approach for physics simulation prediction is proposed with applications in graphics rendering and multimedia. A prediction mechanism is introduced based on regression that aims to reduce the computational cost of simulations in a given scene by negating the need to perform physics calculations every frame. Novel features based on the energy of a scene over time are suggested for the training stage. Experiments were performed to evaluate the performance of the proposed prediction system, indicating that in cases where precision is not essential, regression tools can be utilized providing visually similar kinematics.
[Bar93] BARAFF D.: Non-penetrating rigid body simulation. State of the art reports, May (1993). 1
[BB07] BOEING A., BRÄUNL T.: Evaluation of real-time physics simulation systems. Proceedings of the 5th international conference on . . . 1, 212 (2007), 281–288. 1
[BM11] BOGDAN P., MARCULESCU R.: A fractional calculus approach to modeling fractal dynamic games. IEEE Conference on Decision and Control and European Control Conference (Dec. 2011), 255–260. 2
[DMI11] DELGADO-MATA C., IBÂ?TNEZ J.: Adaptive Physics for Game-Balancing in Video-Games for Social Interaction. 2011 International Conference on Technologies and Applications of Artificial Intelligence (Nov. 2011), 254–259. 2
[Ega03] EGAN K.: Techniques for Real-Time Rigid Body Simulation. 1
[Gou06] GOULD, HARVEY; TOBOCHNIK, JAN; CHRISTIAN W.: Introduction to Computer Simulation Methods: Application to Physical Systems. Addison- Wesley, 2006. 1
[HQZ12] HU W., QU Z., ZHANG X.: A New Approach of Mechanics Simulation Based on Game Engine. Computational Sciences and . . . (2012). 1
[LLS09] LUO F., LIU C., SUN Z.: Intelligent Vehicle Simulation and Debugging Environment Based on Physics Engine. 2009 International Asia Conference on Informatics in Control, Automation and Robotics (Feb. 2009), 329–333. 1
[RSH*13] ROENNAU A., SUTTER F., HEPPNER G., OBERLAENDER J., DILLMANN R.: Evaluation of physics engines for robotic simulations with a special focus on the dynamics of walking robots. 2013 16th International Conference on Advanced Robotics (ICAR) (Nov. 2013), 1–7. 2
[SL06] SAKHANENKO N., LUGER G.: Shock physics data reconstruction using support vector regression. . . . of Modern Physics C 17, 9 (2006), 1313–1325. 2
[SLM06] SERVIN M., LACOURSIERE C., MELIN N.: Interactive simulation of elastic deformable materials. Proceedings of SIGRAD Conference (2006), 22–32. 1
[SM12] SILVA D. F., MACIEL A.: A comparative study of physics engines for modeling soft tissue deformation. 2012 XXXVIII Conferencia Latinoamericana En Informatica (CLEI) (Oct. 2012), 1–7. 2
WHP10] WU J., HUANG L., PAN X.: A novel bayesian additive regression trees ensemble model based on linear regression and nonlinear regression for torrential rain forecasting. 2010 Third International Joint Conference on Computational Science and Optimization (2010), 466–470. 2
