(4d) Modeling of Soft Materials: Model-Driven Versus Data-Driven

The puzzle of how the intelligence arises from the structure of living/soft matter remains standing. With the advancement of experimental discovery and big data, modeling has become the efficient and informative approach to understand the dynamics of the soft materials and build up the complexity. My research interests span three topics around the soft materials:

• Transport phenomena: particle migration in complex fluids gains significant applications in microfluidics and diagnostic devices. I am interested in understanding the sophisticated relationship between particle morphology and polymeric stresses.
• Red blood cells & vesicles: Intricate relationship between suspension dynamics and deformability allows one to understand the biophysics of blood flow and other extracellular vesicles. I employ machine learning approach to understand how velocity fluctuation contribute to the migration of soft matters.
• Chemical reactions: I apply template free fingerprint to understand the reaction pathways between reactants and products.

Research Background

During my Ph.D., I worked with Prof Arezoo Ardekani at the University of Notre Dame and my research topic is about active matter with a focus of flow physics in swimming at microscale (JFM 2012, Phys Fluids 2012, PRE 2013, PRE 2019). I performed supercomputing to explore the mixing generated by swimmers and It has created the numerical framework of high-fidelity simulation for organism swimming in stratified fluids (Sci Rep 2015).

At Purdue, I worked with Prof Sangtae Kim and Prof Vivek Narsimhan to study the dynamics of ellipsoid in complex fluids (Phys Fluids 2019(1,2), JFM 2020(2)) and formed an industrial collaboration with the director of unconventional energy. Currently, I am a research associate and also working with Prof Doraiswami Ramkrishna, Prof Chongli Yuan and Prof Brett Savoie to study a range of subjects related to chemical sciences and translational research such as prediction of chemical reactions, epigenetics, antimicrobial transfer, and develop sampling method for stochastic systems.

Teaching Interests

Many areas of Chemical Engineering, including transport phenomena and quantitative biology, are interdisciplinary and require a strong background in analytical skills such as programming and physics. These analytical skills benefit students as they pursue their goals—whether working as an engineer, being an entrepreneur, or pursuing other professions such as business or law. To attract students to a math program, a well-structured training process is needed to provide students with a solid foundation, keep them engaged, and encourage them to make breakthroughs. My goal as an educator is to strengthen the process of training creative young minds in Applied Mathematics and Data Science. After gaining experience as a teaching assistant and mentoring undergraduates/graduate students at both the University of Notre Dame and Purdue University, I have developed an understanding of how I would like to approach this goal. For undergraduate education, my approaches are (1) active learning in the classes and (2) bridging engineering knowledge with machine learning. For graduate students, my method is (3) helping students acquire in-depth chemical physics and mathematical knowledge.

Selected publications (out of 18 total, google scholar: https://scholar.google.com/citations?user=uEmQO5cAAAAJ&hl=en)

First-author contributions are underlined

Wang, S., Kim, S. (2020) “Revisiting the Top Ten Ways that DDDAS Can Save the World with an Update in the BioInfoSciences Area and on the Energy Bridge” (Part of the Lecture Notes in Computer Science book series (LNCS, volume 12312)). DOI: 10.1007/978-3-030-61725-7_3

Wang, S.†, Ramkrishna, D.† “A model to rate strategies for managing disease due to COVID19 infection” Scientific Reports, 10, 22435. DOI: https://doi.org/10.1038/s41598-020-79817-7

Wang, S., Ramkrishna, D., Narsimhan, V. (2020) Exact sampling of polymer conformations using Brownian bridges. The Journal of Chemical Physics, 153, 034901. DOI: 10.1063/5.0010368

Wang, S., Martin, C.P. and Kim, S., 2019. Improper integrals as a puzzle for creeping flow around an ellipsoid. Physics of Fluids, 31(2), p.021101. (Invited papers on transport phenomena in celebration of Prof. Robert Byron Bird’s 95^th birthday)

Wang, S. and Ardekani, A.M., 2015. Biogenic mixing induced by intermediate Reynolds number swimming in stratified fluids. Scientific reports, 5, p.17448. (Highlighted on Physics.org, Futurity, Geology Page, Purdue College of Engineering News Page, Purdue Research Computing Cluster)

Wang, S. and Ardekani, A.M., 2012. Unsteady swimming of small organisms. Journal of Fluid Mechanics, 702, pp.286-297.

Author

Shiyan Wang

Purdue University