长期模拟物理和机械行为使用课程转移学习基于物理信息的神经网络
Yuan Guo1, Zhuojia Fu2, Jian Min1
1College of Mechanics and Engineering Science, Hohai University, Nanjing 211100, PR China.
概括
本研究介绍了基于课程转移学习的物理信息神经网络 (CTL-PINN),用于准确的长期模拟. CTL-PINN通过结合课程和转移学习来增强物理信息的神经网络,用于复杂的物理和机械行为.
科学领域:
- 计算物理 计算物理
- 机器学习 机器学习
- 科学计算科学计算
背景情况:
- 标准物理信息神经网络 (PINNs) 在长期模拟中面临挑战,包括局部优化和不准确性.
- 像CL-PINN和TL-PINN这样的现有方法在延长时间域的计算效率和准确性方面存在局限性.
研究的目的:
- 提出一种基于课程转移学习的新型物理信息神经网络 (CTL-PINN),用于强大高效的长期模拟.
- 增强PINNs在处理复杂的物理和机械行为的能力.
主要方法:
- 将长期问题分解为连续的短期子问题.
- 在最初的子问题中使用标准PINN.
- 利用课程学习来整合先前步骤中的信息,用于随后的时间域问题.
- 整合转移学习以利用先前的培训数据来解决连续的时间域转移问题.
主要成果:
- CTL-PINN有效结合课程和转移学习,克服了标准PINN的局限性.
- 与现有方法相比,在解决长期计算挑战方面表现出卓越的性能.
- 通过非线性波传播,基尔霍夫板动力学和水库水力动力学的应用来验证.
结论:
- CTL-PINN为物理和机械系统的长期模拟提供了强大而高效的框架.
- 拟议的方法在复杂的动态响应建模中提高了准确性和计算效率.
- CTL-PINN显示出在各种工程和物理领域推进科学计算的巨大潜力.
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