面向刚性吉他弦振动的物理信息神经网络
Towards Physics-Informed Neural Networks for Stiff Guitar String Vibrations
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中文总结 AI 辅助
本研究利用物理信息神经网络模拟刚性吉他弦的振动，通过将控制方程纳入损失函数，并与实验和FDTD模拟对比，验证了PINN在建模刚性弦振动方面的有效性。
中文摘要 AI 辅助
由于刚性弦振动的色散和高频特性，对其建模具有挑战性。本研究探讨了物理信息神经网络（PINNs）在模拟由拨弦引起的具有尖锐初始条件的一维线性刚性弦横向振动方面的有效性。控制偏微分方程（PDE）及其相关的初始条件和边界条件被直接纳入神经网络的损失函数中。为了获得参考测量值，进行了断线实验以激励弦，并使用激光轮廓仪捕获其振动响应。实验测量、时域有限差分（FDTD）模拟和基于PINN的模拟之间的比较显示出良好的整体一致性，凸显了PINN在模拟刚性弦振动方面的潜力。
英文摘要
Modeling stiff string vibrations is challenging due to their dispersive and high-frequency characteristics. This study investigates the effectiveness of Physics-Informed Neural Networks (PINNs) in simulating the transverse vibration of a one-dimensional linear stiff string with sharp initial conditions induced by plucking. The governing Partial Differential Equation (PDE), along with the associated initial and boundary conditions, is incorporated directly into the loss function of the neural network. For the reference measurements, a wire-breaking experiment was performed to excite the string, and its vibration response was captured using a laser profiler. The comparison between experimental measurements, finite-difference time-domain (FDTD) simulations, and PINN-based simulations shows good overall agreement, highlighting the potential of PINNs for modeling stiff-string vibrations.
发表机构
- McGill University（麦吉尔大学）
- Yamaha Corporation（雅马哈公司）
机构由 AI 辅助整理，请以论文原文为准。
