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GNNRL-平滑:用于网格优化的无先验增强学习模型
Zhichao Wang1, Xinhai Chen1, Chunye Gong1
1Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, 410073, China; Laboratory of Digitizing Software for Frontier Equipment, National University of Defense Technology, Changsha, 410073, China.
概括
本研究引入了一种新的无先验增强学习模型,用于网格平滑,提高网格质量和连接性,而没有先前的数据. 该方法显著提高了模拟效率和稳定性.
科学领域:
- 计算几何学的计算几何学
- 计算机辅助工程 计算机辅助工程
- 人工智能的人工智能
背景情况:
- 传统的网格光滑方法难以平衡效率和强度.
- 现有的智能光滑模型通常需要标记的数据集或先前的知识.
- 提高网格连接的有限能力限制了先前方法的有效性.
研究的目的:
- 为智能网格光滑开发一个无先验增强学习模型.
- 为了提高网格质量和连接性.
- 为了提高网格光滑过程的效率和稳定性.
主要方法:
- 对现有的智能光滑学习机制进行系统分析.
- 将图形神经网络与用于节点平滑的强化学习集成.
- 引入基于强化学习的网格连接性改进剂.
- 将网格优化作为马尔科夫决策过程的正式化.
- 使用双延迟深度决定性政策梯度和双决斗深度Q网络的训练代理.
主要成果:
- 在复杂的表面3D网格上实现了维护特征的光滑.
- 在智能光滑方法中,在2D网格上展示了最先进的结果.
- 超越了基于优化的传统光滑方法的7.16倍的速度.
- 通过连接改善代理有效提高网格质量分配.
结论:
- 拟议的先前无强化学习模型为智能网格光滑提供了一种有效的方法.
- 集成的节点光滑和连接改善剂显著提高了网格质量.
- 该模型为模拟中的网格优化提供了强大,高效和数据独立的解决方案.
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