Physics-Informed Neural Networks (PINN) for Enhanced Dynamic Modeling and Reverse Problem Solving in an Electro-Wetting Operated Microfluid Prism

Physics-Informed Neural Networks (PINNs) are transforming the field of machine learning, offering powerful solutions to complex problems in fluid dynamics. This research introduces the application of PINNs to the intricate challenges associated with Electrowetting On Dielectric (EWOD) operated microliquid prisms, a task that has traditionally posed significant hurdles for conventional numerical methods.

We delve into the potential of PINNs, noting their robustness and precision in the face of complexity. With optimized PINN configurations, we successfully model the intricate dynamics of EWOD operated prisms, and understand the fluid-structure interactions within this context. By integrating physical laws into PINN learning, we enhance data efficiency, generalization, and interpretability.

Importantly, our application of PINNs to this scenario enables us to tackle inverse problems, providing unique insights into relationships like the interplay between voltage and geometry. This clearer understanding of variable interrelationships in hydrodynamic problems underscores the transformative potential of PINNs, suggesting their capability to supplement or even replace traditional numerical methods in fluid dynamics problem-solving. The adoption of PINNs could indeed signify a fundamental shift in our approach, leading to significant advancements in the field.