3 papers
physics.flu-dyn2024
Predicting Performance of Microfluidic-Based Alginate Microfibers with Feature-Supplemented Deep Neural Networks
Nicholus R. Clinkinbeard, Justin Sehlin, Meharpal Singh Bhatti +3
Selection of solution concentrations and flow rates for the fabrication of microfibers using a microfluidic device is a largely empirical endeavor of trial-and-error, largely due t…
cs.CE2024
Accelerating Hydrodynamic Fabrication of Microstructures using Deep Neural Networks
Nicholus R. Clinkinbeard, Reza Montazami, Nicole N. Hashemi
Manufacturing of microstructures using a microfluidic device is a largely empirical effort due to the multi-physical nature of the fabrication process. As such, models are desired…
cs.ET2022
Supplementation of deep neural networks with simplified physics-based features to increase model prediction accuracy
Nicholus R. Clinkinbeard, Nicole N. Hashemi
To improve predictive models for STEM applications, supplemental physics-based features computed from input parameters are introduced into single and multiple layers of a deep neur…