Mechanical Engineering, Electrical and Computer Engineering
The 1st International Workshop "Feature Extraction: Modern Questions and Challenges"
Journal or Book Title
Proceedings of Machine Learning Research
Neural Information Processing Systems (NIPS) 2015
December 7-12, 2015
Montreal, Quebec, CA
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 patterns in order to solve inverse problems in fluid mechanics. A recent discovery showed the ability to control the fluid deformations in a microfluidic channel by placing a sequence of pillars. This provides a fundamental tool for numerous material science, manufacturing and biological applications. However, designing pillar sequences for user-defined deformations is practically infeasible as the current process requires laborious and time-consuming design iterations in a very large, highly nonlinear design space that can have as large as 1015 possibilities. We demonstrate that hierarchical feature extraction can potentially lead to a scalable design tool via learning semantic representations from a relatively small number of flow pattern examples. The paper compares the performances of pre-trained deep neural networks and deep convolutional neural networks as well as their learnt features. We show that a balanced training data generation process with respect to a metric on the output space improves the feature extraction performance. Overall, the deep learning based design process is shown to expedite the current state-of-the-art design approaches by more than 600 times.
Kin Gwn Lore, Daniel Stoecklein, Michael Davies, Baskar Ganapathysubramanian, Soumik Sarkar.
Lore, Kin Gwn; Stoecklein, Daniel; Davies, Michael; Ganapathysubramanian, Baskar; and Sarkar, Soumik, "Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns" (2015). Materials Science and Engineering Conference Papers, Posters and Presentations. 38.