Principal Investigator: Joel Harley
Start Date: October 1, 2018
End Date: September 30, 2020
This project studies physics-informed neural networks to characterize and monitor materials and engineered systems with ultrasound. Ultrasound is studied because it is a wireless, high resolution, medically safe, and inherently secure technology that is driving innovations in wearables, medical implants, secure / encrypted communication systems, and imaging. The project’s neural network algorithms can characterize materials and engineered systems from the micro-level (e.g., micro-electrical-mechanical systems) to the macro-level (e.g., pipelines, airplanes, or rail lines). The neural networks characterize the materials by learning the general behavior of ultrasound from simulated data, physical constraints, and measured data. That learned behavior is then compared with the true measured behavior. To achieve our goal, three significant challenges of applying neural networks (and machine learning generally) to many engineered systems are studied: (1) experimental training data is often scarce or unavailable, (2) data diversity and variability is typically high, and (3) purely data-driven approaches offer few engineering assurances. Data scarcity is addressed by training neural networks with simulations rather than experimental data. Data diversity and variability is addressed by using transfer learning theory to transfer generalized knowledge from the simulation data into the analysis of the experimental data. Engineering assurances are improved by incorporating physics-based constraints into the neural networks. The resulting neural networks are referred to as Helmholtz networks, named for the time-independent wave equation.
The objective of the project is to establish the foundation for Helmholtz networks, which are deep, generative, physics-informed neural networks that reconstruct ultrasonic wave propagation and locate ultrasonic sources. The Helmholtz networks are based on the fact that each frequency of a wave can be represented as the sum of a sparse number of spatial modes. The modes are constrained by the Helmholtz equation and this physical constraint ensures that the machine learning algorithm is trustworthy for system-critical engineered systems (e.g., health monitoring of an aircraft). Such physics-informed machine learning is an important (albeit not widely studied) topic for integrating advanced computation tools into real-time engineered systems.