We employ physics-informed neural networks (PINNs) to quantify the microstructure of polycrystalline nickel by computing the spatial variation of compliance coefficients (compressibility, stiffness, and rigidity) of the material. The PINNs are supervised with realistic ultrasonic surface acoustic wavefield data acquired at an ultrasonic frequency of 5 MHz for the polycrystalline material. The ultrasonic wavefield data are represented as a deformation on the top surface of the material with the deformation measured using the method of laser vibrometry. The ultrasonic data are further complemented with wavefield data generated using a finite-element-based solver. The neural network is physically informed by the in-plane and out-of-plane elastic wave equations, and its convergence is accelerated using adaptive activation functions. The overarching goal of this work is to infer the spatial variation of compliance coefficients of materials using PINNs, which for ultrasound involves the spatially varying speed of the elastic waves. More broadly, the resulting PINN-based surrogate model shows a promising approach for solving ill-posed inverse problems, often encountered in the nondestructive evaluation of materials.

中文翻译：

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使用超声数据量化多晶镍微观结构特性的物理信息神经网络：解决反演问题的一种有前景的方法


我们采用物理信息神经网络 (PINN) 通过计算材料的柔量系数（压缩性、刚度和刚度）的空间变化来量化多晶镍的微观结构。 PINN 受到以 5 MHz 超声波频率采集的多晶材料真实超声波表面声波场数据的监控。超声波场数据表示为材料顶表面上的变形，该变形是使用激光测振法测量的。超声数据进一步补充了使用基于有限元的求解器生成的波场数据。神经网络在物理上由面内和面外弹性波方程提供信息，并使用自适应激活函数加速其收敛。这项工作的总体目标是使用 PINN 推断材料的柔量系数的空间变化，对于超声波而言，这涉及弹性波的空间变化速度。更广泛地说，由此产​​生的基于 PINN 的代理模型显示了一种解决不适定反问题的有前途的方法，这些问题在材料的无损评估中经常遇到。