Tags: uncertainty quantification

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  1. Interval Deep Learning for Uncertainty Quantification in Engineering Problems

    10 Jun 2021 | Contributor(s):: Betancourt, David

    Advisors: Rafi L Muhanna, B. Aditya Prakash, Chao Zhang, Abdul-Hamid Zureick, Steffen Freitag, Vladik KreinovichDeep neural networks are becoming more common in important real-world safety-critical applications where reliability in the predictions is paramount. Despite their exceptional...

  2. Uncertainty Informed Integrated Computational Materials Engineering for Design and Development of Fatigue Critical Alloys

    11 Jan 2021 | Contributor(s):: Whelan, Gary Francis

    Advisors: David L. McDowell, Richard Neu, Laura Swiler, Yan Wang, Hamid GarmestaniUncertainty is intrinsically tied to decision-making in design. Process-Structure-Property (PSP) relations are central to development of new and improved materials. The multitude of PSP linkages for any performance...

  3. Multiscale Uncertainty Quantification for Physics-Based Data-Driven Materials Design and Optimization

    14 Jan 2020 | Contributor(s):: Tran, Anh Vuong

    Advisors: Yan Wang, David L. McDowell, Chaitanya Deo, Hongyuan Zha, Xin SunUncertainty is a critical element in computational materials science. From the experimental perspective, the sources of uncertainty include measurement errors caused by instrument, operator, and sensing models, as well as...

  4. Berkay Yucel


  5. Multiscale Modeling and Uncertainty Analysis of Mechanical Behavior of Nanostructural Metals

    07 Jun 2017 | Contributor(s):: Zeng, Zhi

    Advisors: Ting Zhu, David McDowell, Chaitanya Deo, Christopher Saldana, Hamid GarmestaniMetals with heterogeneous nanostructures hold great promise for achieving a synergy of ultra-high strength and ductility, thus overcoming the conventional strength-ductility tradeoff in nanostructured...

  6. May 17 2017

    Materials Innovation Lecture Series — Uncertainty Quantification with Thoughts on Multiscale Materials Applications

     Materials Innovation Lecture SeriesDate and Time: Wednesday, May 17, 2017 - 3:00pm-4:00pmLocation: The George W. Woodruff School of Mechanical Engineering, MRDC Bldg., Room 4211, 801 Ferst Drive,...


  7. A Multi-Level Upscaling and Validation Framework for Uncertainty Quantification in Additively Manufactured Lattice Structures

    11 Jan 2017 | Contributor(s):: Gorguluarslan, Recep Muhammet

    Advisors: Seung-Kyum Choi, David W. Rosen, David L. McDowell, Christopher J. Saldana, Rafi L. MuhannaMultiscale modeling techniques are playing an ever increasing role in the effective design of complex engineering systems including aircraft, automobiles, etc. Lightweight cellular lattice...

  8. Introduction to Bayesian Linear Regression (BLR) and Gaussian Process Regression (GPR)

    02 Dec 2016 | | Contributor(s):: Yuksel Yabansu

    Slides about Bayesian Linear Regression (BLR) and Gaussian Process Regression (GPR) shown on Wednesday, 30th November, 2016 

  9. Xinyi Gong

    Xinyi GongXinyi Gong is currently pursuing a PhD in Materials Science and Engineering at Georgia Institute of Technology. He earned a bachelor's degree in Materials Science and Engineering at...


  10. Andrew James Medford


  11. Cross-Scale Model Validation With Aleatory and Epistemic Uncertainty

    08 Jun 2015 | | Contributor(s):: Blumer, Joel David

    Advisors: Yan Wang, David L. McDowell, Laura P. SwilerNearly every decision must be made with a degree of uncertainty regarding the outcome. Decision making based on modeling and simulation predictions needs to incorporate and aggregate uncertain evidence. To validate multiscale simulation...