App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 10

Physics informed learning model for multi-material simulation

Dou Kewei
Zhu Jian
Chen Bingfeng
Cai Ruichu
School of Computers, Guangdong University of technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2023.12.0637

Abstract

Physics-Based Animation (PBA) , which aims to iteratively generate multiple frames of physical phenomenon animations based on the initial frame information. Existing deep learning based material simulation models usually improve model fitting ability through encoders and decoders, but fail to fully utilize the physical information of the materials themselves, resulting in poor simulation effectiveness and accuracy. To address this problem, we propose a material simulation model based on physical information (PILMMS) . This model divides the entire schedule into multiple functional modules based on physical concepts, including internal force, external force, boundary processing, and so on. Design each functional module based on its physical concepts and mechanisms, allowing them to integrate the corresponding physical information through input features. Furthermore, the various modules implemented based on convolutional networks can fully utilize local region information. In addition, dimension expansion and reduction are introduced into the convolution module. This ensures the accuracy of information while compressing it. The model is used to learn materials such as liquid, sand, snow, etc and generate corresponding results. It verifies that our model has strong information extraction ability and outperforms existing models in objective evaluation indicators.

Foundation Support

国家自然科学基金资助项目(62237001)
国家重点研发计划项目(2021ZD0111501)
国家自然科学基金优秀青年基金资助项目(6212200101)
国家自然科学基金面上项目(62272298,62176066,61976052)
广州市科技计划项目(202002030110,202007040005)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.12.0637
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 10

Publish History

[2024-03-28] Accepted Paper

Cite This Article

窦可为, 朱鉴, 陈炳丰, 等. 融合物理信息的多材料模拟学习模型 [J]. 计算机应用研究, 2024, 41 (10). (2024-07-12). https://doi.org/10.19734/j.issn.1001-3695.2023.12.0637. (Dou Kewei, Zhu Jian, Chen Bingfeng, et al. Physics informed learning model for multi-material simulation [J]. Application Research of Computers, 2024, 41 (10). (2024-07-12). https://doi.org/10.19734/j.issn.1001-3695.2023.12.0637. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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