Designing acoustofluidic devices using a simplified physics-informed machine learning approach

Abstract

The safe positioning of particles within an acoustofluidic device is critical in biomedical and biological applications. Relating the design of acoustofluidic device walls and the internal acoustic field is a complex, nonlinear problem. The field of Physics-Informed Machine Learning (PIML) offers a number of potential approaches to simplify the design of these devices. One such PIML approach is learning from synthetic data. With large scientific data sets with rich spatial-temporal data and high-performance computing providing large amounts of data to be inferred and interpreted, the task of PIML is to ensure that these predictions and inferences are enforced by, and conform to the limits imposed by physical laws. The tools employed in PIML can include large, deep neural networks, Bayesian modeling, and deep reinforcement learning with sophisticated simulations of the environment. In this work, we show a simplified version of PIML using a combination of a small fully connected neural network and a 2D meshfree simulator of acoustic devices to predict the boundary shape for an acoustically actuated device. We will discuss the real-world results and applications, as well as the current limitations of this approach and the path ahead to scale and include more complexity for more applications and designs.