Logo: University of Southern California

Events Calendar



Select a calendar:



Filter November Events by Event Type:



Events for November 12, 2019

  • ISE 651 - Epstein Seminar

    Tue, Nov 12, 2019 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Yong Huang, Professor, University of Florida

    Talk Title: Bioprinting: Implementation, Process Dynamics, and Process-Induced Cell Injury

    Host: Prof. Yong Chen

    More Information: November 12, 2019.pdf

    Location: Ethel Percy Andrus Gerontology Center (GER) - 206

    Audiences: Everyone Is Invited

    Posted By: Grace Owh

    OutlookiCal
  • Astani Civil and Environmental Engineering Seminar

    Tue, Nov 12, 2019 @ 04:00 PM - 05:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Qizhi He, Pacific Northwest National Laboratory (PNNL)

    Talk Title: Machine Learning Enhanced Computational Mechanics: Reduced-Order Modeling and Physics-Informed Data-Driven Computing

    Abstract:
    Advances in the field of machine learning and the increasing availability of data from laboratory/field observations and high-fidelity simulation are quickly changing how the world solves important scientific problems. In this talk, I will present our research on developing integrative computational methods that leverage both physics-based models and data-centric techniques to address various challenges in computational mechanics related to civil, mechanical, and biological applications.

    First, I will introduce physics-preserving reduced order schemes that employ simulation data and subspace projection to reduce computational cost while preserving critical physical properties in modeling fracture mechanics and thermal fatigue of electronic packages. Second, I will describe a manifold learning-based data-driven computing approach for modeling complex materials, where physics-based simulation proceeds interactively with the local data manifold reconstructed from experimental data,circumventing the limitations of using phenomenological constitutive models.

    Finally, I will discuss our recent work in PNNL on developing machine learning framework based on deep neural networks for discovering hidden physics and modeling subsurface flow and transport in heterogenous porous media. The proposed approach allows the seamless fusion and integration of measurements from multiphysics systems and the information provided by the physical conservation laws, which regularize the neural networks as informative priors. The effectiveness of meshfree methods for these data-driven computing will also be demonstrated.
    This work is supported by NSF, US Army Engineer Research, and the DOE ASCR.


    Biography: Dr. Qizhi He is currently a Postdoctoral Research Associate in the Advanced Computing, Mathematics and Data Division at Pacific Northwest National Laboratory (PNNL). He obtained his B.S. in Engineering Mechanics from Wuhan University, M.S. in Computational Mechanics from Dalian University of Technology, and M.A. in Applied Mathematics and Ph.D. in Structural Engineering from the University of California, San Diego (UCSD). His research mainly focuses on the development of machine learning enhanced computational methods to model complex biological and civil systems and provide solutions to protect real-life structures from extreme hazardous events. His work combines the concepts from physics-based modeling, machine learning, and meshfree type approximation and discretization

    Host: Dr. Roger Ghanem

    Location: Kaprielian Hall (KAP) - 209

    Audiences: Everyone Is Invited

    Posted By: Evangeline Reyes

    OutlookiCal