Deep Learning for Flow Sculpting: Insights into Efficient Learning using Scientific Simulation Data

Scientific Reports

Volumn 7, Issue , 2017, Pages

  Stoecklein, Daniel ^a   Lore, Kin Gwn ^a   Davies, Michael ^a   Sarkar, Soumik ^a   Ganapathysubramanian, Baskar ^a  

^a Iowa State University of Science and Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN; LEARNING; PREDICTION; SAMPLING; SIMULATION;

EID: 85017419380     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep46368     Document Type: Article

References (43)

1. Neto, F. D. M. & Neto, A. J. d. S. An Introduction to Inverse Problems with Applications (Springer, 2013).
2. Vogel, C. Computational Methods for Inverse Problems (Society for Industrial and Applied Mathematics, 2002).
3. Amini, H. et al. Engineering fluid flow using sequenced microstructures. Nature communications 4, 1826 (2013).
4. Stoecklein, D. et al. Micropillar sequence designs for fundamental inertial flow transformations. Lab Chip 14, 4197-4204 (2014).
5. Nunes, J. K. et al. Fabricating shaped microfibers with inertial microfluidics. Advanced Materials 26, 3712-3717 (2014).
6. Paulsen, K. S., Di Carlo, D. & Chung, A. J. Optofluidic fabrication for 3D-shaped particles. Nature Communications 6, 6976 (2015).
7. Paulsen, K. S. & Chung, A. J. Non-spherical particle generation from 4D optofluidic fabrication. Lab Chip (2016).
8. Wu, C. Y., Owsley, K. & Di Carlo, D. Rapid Software-Based Design and Optical Transient Liquid Molding of Microparticles. Advanced Materials 7970-7978 (2015).
9. Sollier, E. et al. Inertial microfluidic programming of microparticle-laden flows for solution transfer around cells and particles. Microfluidics and Nanofluidics (2015).
10. Uspal, W. E., Burak Eral, H. & Doyle, P. S. Engineering particle trajectories in microfluidic flows using particle shape. Nature communications 4, 2666 (2013).
11. Griffin, D. R., Weaver, W. M., Scumpia, P. O., Di Carlo, D. & Segura, T. Accelerated wound healing by injectable microporous gel scaffolds assembled from annealed building blocks. Nature materials 14, 737-744 (2015).
12. Fiorini, F. et al. Nanocomposite Hydrogels as Platform for Cells Growth, Proliferation, and Chemotaxis. Small 4881-4893 (2016).
13. Amini, H., Lee, W. & Di Carlo, D. Inertial microfluidic physics. Lab Chip 14, 2739-2761 (2014).
14. Zhang, J. et al. Fundamentals and Applications of Inertial Microfluidics: A Review. Lab Chip 16, 10-34 (2016).
15. Lu, M. et al. Microfluidic hydrodynamic focusing for synthesis of nanomaterials. Nano Today 11, 778-792 (2016).
16. Stoecklein, D., Wu, C.-y., Kim, D., Di Carlo, D. & Ganapathysubramanian, B. Optimization of micropillar sequences for fluid flow sculpting. Physics of Fluids 28 (2016).
17. Stoecklein, D., Davies, M., Wubshet, N., Le, J. & Ganapathysubramanian, B. Automated design for microfluid flow sculpting: multiresolution approaches, efficient encoding, and CUDA implementation. ASME Journal of Fluids Engineering 139 (2017).
18. Lore, K. G., Stoecklein, D., Davies, M., Ganapathysubramanian, B. & Sarkar, S. Hierarchical feature extraction for efficient design of microfluidic flow patterns, JMLR: Workshop and Conference Proceedings 44 Proceedings of The 1st International Workshop on "Feature Extraction: Modern Questions and Challenges", NIPS, pp. 213-225 (2015).
19. Bengio, Y., LeCun, Y. et al. Scaling learning algorithms towards ai. Large-scale kernel machines 34 (2007).
20. Zeiler, M. D. & Fergus, R. Visualizing and Understanding Convolutional Networks. ECCV (2014).
21. Rabitz, H., Alis, Ö. & Aliş, Ö. F. General foundations of high-dimensional model representations. J. Math. Chem. 25, 197-233 (1999).
22. Squires, T. M. & Quake, S. R. Microfluidics: Fluid physics at the nanoliter scale. Reviews of Modern Physics 77, 977-1026 (2005).
23. Deng, L. & Dong, Y. Foundations and trends in signal processing. Signal Processing 7, 3-4 (2014).
24. Bengio, Y., Courville, A. & Vincent, P. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1798-1828 (2013).
25. Hinton, G. & Salakhutdinov, R. Reducing the dimensionality of data with neural networks. Science 313, 5786, 504-507 (2006).
26. Salakhutdinov, R., Mnih, A. & Hinton, G. Restricted Boltzmann Machines for Collaborative Filtering. Proceedings of the 24th ACM International Conference on Machine Learning (ICML) 791-798 (2007).
27. Coates, A., Ng, A. Y. & Lee, H. An analysis of single-layer networks in unsupervised feature learning. In International Conference on Artificial Intelligence and Statistics, 215-223 (2011).
28. Salakhutdinov, R. & Hinton, G. Replicated softmax: An undirected topic model. Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference 1607-1614 (2009).
29. Larochelle, H. & Bengio, Y. Classification using discriminative restricted boltzmann machines. In Proceedings of the 25th international conference on Machine learning, 536-543 (ACM, 2008).
30. Guo, X., Li, W. & Iorio, F. Convolutional neural networks for steady flow approximation. In Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2016).
31. Akintayo, A., Lore, K. G., Sarkar, S. & Sarkar, S. Prognostics of combustion instabilities from hi-speed flame video using a deep convolutional selective autoencoder. International Journal of Prognostics and Health Management 7 (2016).
32. Lore, K. G., Akintayo, A. & Sarkar, S. LLNet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recognition 61, 650-662 (2017).
33. Lore, K. G., Sweet, N., Kumar, K., Ahmed, N. & Sarkar, S. Deep value of information estimators for collaborative human-machine information gathering. International Conference on Cyber-physical Systems (ICCPS). Vienna, Austria (2016).
34. Singh, A., Ganapathysubramanian, B., Singh, A. K. & Sarkar, S. Machine learning for high-throughput stress phenotyping in plants. Trends in plant science 21, 110-124 (2016).
35. Zeng, H., Edwards, M. D., Liu, G. & Gifford, D. K. Convolutional neural network architectures for predicting dna-protein binding. Bioinformatics 32, i121-i127 (2016).
36. Baldi, P., Sadowski, P. & Whiteson, D. Searching for exotic particles in high-energy physics with deep learning. Nature communications 5, 4308 (2014).
37. Sadowski, P. J., Whiteson, D. & Baldi, P. Searching for higgs boson decay modes with deep learning. In Advances in Neural Information Processing Systems, 2393-2401 (2014).
38. Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. In NIPS (2012).
39. John Aldo Lee, M. V. Nonlinear dimensionality reduction (Springer, 2007).
40. Xie, Y., Wodo, O. & Ganapathysubramanian, B. Incompressible two-phase flow: Diffuse interface approach for large density ratios, grid resolution study, and 3D patterned substrate wetting problem. Computers and Fluids 141, 223-234 (2016).
41. Matsumoto, M. & Nishimura, T. Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. 8, 3-30 (1998).
42. Sobol, I. M., Asotsky, D., Kreinin, A. & Kucherenko, S. Construction and comparison of high-dimensional sobol generators. Wilmott Journal 64-79 (2011).
43. Theano Development Team. Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints abs/1605.02688 (2016).