[Oral Presentation]基于机器学习的颗粒过程高效仿真及逆向设计

Organized By

Hohai University

Monash University

Co-organized by

Southeast University

Jiangxi University of Science and Technology

Northeast University

DUT-BSU Scientific Research and Innovation Center

Suzhou University of Science and Technology

State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment

Sponsoring agency

KELI SDEM Software (Dalian) Co., Ltd.

Sugon Smart Computing Information Technology Co., Ltd.

Beijing Paratera Technology Co., Ltd.

Simpop Information Technology Co., Ltd.

Important Dates

Conference Announcement

December 2023, release First Round Announcement
Abstract Submission Deadline
March 31^th, 2024
Paper Submission Deadline
April 30^th, 2024
Registration
From January 1^st, 2024
Conference Schedule
May 31^st (Friday), 2024 On-Site Registration
June 1^st to 3^rd, 2024 Conference

基于机器学习的颗粒过程高效仿真及逆向设计

ID：40 Submission ID：113 View Protection：ATTENDEE Updated Time：2024-04-09 22:06:31 Hits：428 Oral Presentation

Start Time：Pending （Asia/Shanghai）

Duration：Pending

Session：[No Session] » [No Session Block]

No files

Abstract

颗粒流动在许多工业过程和自然现象中起着关键作用，对于快速和精确的模拟提出了重大挑战。传统的模拟技术，尤其是使用离散元方法（DEM）的技术，对于工业过程计算量需求巨大。近期人工智能在各个科学工程领域的成功应用预示着一场变革性的转变，引发了利用机器学习进行颗粒流模拟的浓厚兴趣。在这项研究中，我们探讨了应用最先进的机器学习方法，如图神经网络（GNNs），学习颗粒之间的相互作用，并开发相应的简化模型，以高效地模拟颗粒流动。开发的方法减少了计算需求，并保持了在预测颗粒流特性方面的高精度。我们进一步探索了机器学习方法在液固搅拌槽和气固流化床的背景下的应用，展示了基于机器学习的代理模型在捕捉颗粒系统复杂动态的多功能性和潜力。

Keywords

机器学习,图神经网络,反向优化,离散元,颗粒流

Sponsored By

Hohai University

Panel of Computational Mechanics on Granular Materials, Working Party of Computational Mechanics, Chinese Society of Theoretical and Applied Mechanics

Jiangsu Society of Theoretical and Applied Mechanics