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聚合物中的物理信息神经网络:一篇评论
Ivan Malashin1, Vadim Tynchenko1, Andrei Gantimurov1
1Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia.
Polymers
|April 26, 2025
概括
基于物理学的神经网络 (PINNs) 为聚合物建模提供了一种新的方法,将数据与物理定律集成在一起. 这篇评论探讨了PINNs.
科学领域:
- 聚合物科学与工程 聚合物科学与工程
- 计算材料科学科学 计算材料科学
- 化学中的人工智能.
背景情况:
- 聚合物系统表现出复杂的多尺度行为,对传统的建模和模拟技术构成重大挑战.
- 精度和计算效率的平衡仍然是聚合物系统中原子和宏观尺度之间的桥梁的关键障碍.
研究的目的:
- 审查物理信息神经网络 (PINNs) 在聚合物科学中的发展和应用.
- 总结PINNs在聚合物建模方面的最新进展,方法,好处和局限性.
- 确定使用PINNs的先进聚合物模拟的未来研究方向.
主要方法:
- 对应于聚合物系统的PINNs现有文献的审查.
- 分析将数据驱动学习与物理定律相结合的方法.
- 评估PINNs用于财产预测,结构设计和流程优化.
主要成果:
- 通过将数据驱动的见解与物理原理相结合,PINNs在克服传统方法的局限性方面表现出前途.
- 应用范围包括聚合物性能预测,结构设计和过程优化.
- 确定了关键的方法和好处,以及目前的局限性.
结论:
- PINNs代表了用于先进的聚合物建模和模拟的强大新兴工具.
- 需要进一步的研究来应对当前的挑战,并充分利用PINNs在聚合物科学中的潜力.
- 通过PINN,可以在多个尺度上进行更准确,更有效的模拟.
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