PEARS: Physics extension and representation through semantics

Benjamin Eckstein, Jean-Luc Lugrin, Dennis Wiebusch, Marc Erich Latoschik
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引用次数: 3

Abstract

Today's physics engines mainly simulate classical mechanics and rigid body dynamics, with some late advances also capable of simulating massive particle systems and some approximations of fluid dynamics. An accurate numerical simulation of complex nonmechanical processes in real time is beyond the state of the art in the respective fields. This paper illustrates an alternative approach to a purely numerical solution. It uses a semantic representation of physical properties and processes as well as a reasoning engine to model cause and effect between objects, based on their material properties. Classical collision detection is combined with semantic rules to model various physical processes, for example, in the areas of thermodynamics, electrodynamics, and fluid dynamics as well as chemical processes. Each process is broken down into fine-grained subprocesses capable of approximating continuous transitions with discretized state changes. Our system applies these high-level state descriptions to low-level value changes, which are directly mapped to a graphical representation of the scene. We demonstrate our framework's ability to support multiple complex, causally connected physical and chemical processes by simulating a Goldberg machine. Our performance benchmarks validate its scalability and potential application for entertainment or edutainment purposes.
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通过语义的物理扩展和表示
今天的物理引擎主要模拟经典力学和刚体动力学,最近的一些进展也能够模拟大质量粒子系统和一些近似流体动力学。对复杂的非机械过程进行精确的实时数值模拟,在各自的领域都是目前最先进的。本文说明了纯数值解的另一种方法。它使用物理属性和过程的语义表示,以及一个推理引擎,根据物体的材料属性对物体之间的因果关系进行建模。经典的碰撞检测与语义规则相结合来模拟各种物理过程,例如,在热力学、电动力学、流体动力学以及化学过程等领域。每个过程被分解成细粒度的子过程,这些子过程能够近似于具有离散状态变化的连续转换。我们的系统将这些高级状态描述应用于低级值变化,这些变化直接映射到场景的图形表示。通过模拟Goldberg机器,我们展示了我们的框架支持多个复杂的、因果关联的物理和化学过程的能力。我们的性能基准验证了它的可扩展性和潜在的娱乐或教育目的的应用程序。
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来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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