A physics-informed neural network for quantifying the microstructure properties of polycrystalline Nickel using ultrasound data

Abstract

We employ physics-informed neural networks (PINNs) to quantify the microstructure of a polycrystalline Nickel by computing the spatial variation of compliance coefficients (compressibility, stiffness and rigidity) of the material. The PINN is supervised with realistic ultrasonic surface acoustic wavefield data acquired at an ultrasonic frequency of 5 MHz for the polycrystalline material. The ultrasonic wavefield data is represented as a deformation on the top surface of the material with the deformation measured using the method of laser vibrometry. The ultrasonic data is 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 non-destructive evaluation of materials.

arXiv comment18 pages, 5 figures

References in corpus (4)

• Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
• On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
• Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks
• Multi-scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains

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