Daniel Stoecklein

Learn More
Deep neural networks are being widely used for feature representation learning in diverse problem areas ranging from object recognition and speech recognition to robotic perception and human disease prediction. We demonstrate a novel, perhaps the first application of deep learning in mechanical design, specifically to learn complex microfluidic flow(More)
Deep learning (DL) became the method of choice in recent years for solving problems ranging from object recognition and speech recognition to robotic perception and human disease prediction. In this paper, we present a hybrid architecture of convolutional neural networks (CNN) and stacked autoencoders (SAE) to learn a sequence of actions that nonlinearly(More)
  • 1