Dear Colleagues, I encourage you to attend Guang Lin<https://www.math.purdue.edu/~lin491/>’s BNC faculty lunch seminar (tomorrow Thursday Feb. 13, 12noon-1pm, Birck 2001). This is to broaden interactions and bring together experimental and modeling groups. Guang applies machine learning techniques to complex engineering problems. He is also the Director of Data Science Consulting Service at Purdue (modeled after the successful statistics consulting services). I hope you are able to make it. Thanks, Ali ************************** Uncertainty Quantification and Scientific Machine Learning for Complex Engineering Systems Guang Lin Mathematics and Mechanical Engineering Purdue Abstract: Experience suggests that uncertainties often play an important role in quantifying the performance of complex systems. Therefore, uncertainty needs to be treated as a core element in the modeling, simulation, and optimization of complex systems. In this talk, I will first present a review of the novel UQ techniques I developed to conduct stochastic simulations for very large-scale complex systems. First, I will present how to employ deep neural network to build a Processing-Microstructure-Mechanical Properties Relationship. In particular, we will use a fibre-reinforced polymer composite material as an example on predicting stress field based on material’s microstructure and loading condition. In addition, a robust data-driven discovery of physical laws with confidence will be introduced. Discovering governing physical laws from noisy data is a grand challenge in many science and engineering research areas. I will present a new Bayesian approach to data-driven discovery of ODEs and PDEs. The new approach will be demonstrated through a wide range of problems, including Navier–Stokes equations. In addition, solving PDEs and predicting material fracture in a fundamentally different way will be discussed. I will present a new paradigm in solving linear and nonlinear PDEs on varied domains without the use of the classical numerical discretization. Instead, we infer the solution of PDEs using a convolutional neural network with quantified uncertainty. The proposed neural network can predict the solution and its uncertainty simultaneously on-the-fly. Finally, I will introduce a new convolutional neural network named Peri-Net we developed to predict and analyze fracture patterns on a disk in real time. I will present and validate the results using the molecular dynamic collision simulations. Bio: GUANG LIN received his M.S. and Ph.D. degrees in applied mathematics from Brown University. He was a Senior Research Scientist at Pacific Northwest National Laboratory from 2008 to 2014. He is currently Director of Data Science Consulting Service, Dean’s Fellow at College of Science, University Scholar, an Associate Professor at the Department of Mathematics, school of Mechanical Engineering, Department of Statistics (Courtesy), Department of Earth, Atmospheric, and Planetary Sciences (Courtesy) at Purdue University. He received NSF faculty early career development award (NSF, 2016), Mid-Career Sigma Xi Award, University Faculty Scholar award (Purdue, 2019), Mathematical Biosciences Institute Early Career Award (MBI, 2015), Ronald L. Brodzinski Award for Early Career Exception Achievement, Department of Energy Pacific Northwest National Laboratory Early Career Award (PNNL, 2012), and Department of Energy Advanced Scientific Computing Research Leadership Computing Challenge award (DOE, 2010). He has had in-depth involvement in developing big data analysis, deep learning and uncertainty quantification tools for a large variety of domains including energy and environment. His research interests include diverse topics in computational science both on algorithms and applications, uncertainty quantification, large-scale data analysis, and multiscale modeling in a large variety of domains. Dr. Lin is currently Associate Editor of Society for Industrial and Applied Mathematics Multiscale Modeling and Simulations. Please find below the BNC Spring Faculty Seminar Series schedule. Date Faculty Topic 2/13/2020 Guang Lin Computational and predictive science and statistical learning both on algorithms and applications 2/20/2020 Lia Stanciu Design and fabrication of biosensors and chemical sensors 2/27/2020 Sunil Bhave Micromachining YIG 3/5/2020 Shriram Ramanathan Brain-inspired computing 3/12/2020 Dallas Morisette FinFET inspired silicon carbide power MOSFETs 3/19/20 Spring Break 3/26/20 Lunch with Dr. Moira Gunn Host of NPR’s Tech Nation and BioTech Nation Discovery Park Lecturer and Shark Tank Competition Judge 4/2/2020 Sophie Lelièvre 3D3C Cell Culture 4/9/2020 Jianguo Mei Challenges and Opportunities in R2R Manufacturing and Commercialization of Thin Film Electrochromics 4/16/2020 NanoDays 4/23/2020 Chi Hwan Lee Sticker-like Electronics (Sticktronics) for Wearable Health Monitoring 4/30/2020 Chen-Lung Hung Ultracold quantum gas and quantum optics 5/7/2020 Finals Week