4 papers
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…
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…
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…
Machine Learning-Assisted E-jet Printing of Organic Flexible Biosensors
Mehran Abbasi Shirsavar, Mehrnoosh Taghavimehr, Lionel J. Ouedraogo +4
Electrohydrodynamic-jet (e-jet) printing technique enables the high-resolution printing of complex soft electronic devices. As such, it has an unmatched potential for becoming the…