Tags: machine learning

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  1. A Framework for the Optimization of Doctrine and Systems in Army Air Defense Units Using Predictive Models of Stochastic Computer Simulations

    07 Jun 2017 | Contributor(s):: Wade, Brian M.

    Advisors: Daniel Schrage, Dimitri Mavris, Lakshmi Sankar, Apinut Sirirojvisuth, David KnudsonThis thesis presents a new methodology that can be used to address large-scale raids made up of different types of Theater Ballistic Missiles (TBMs) and Cruise Missiles (CMs) that attempt to overwhelm the...

  2. A Framework for Understanding and Controlling Batch Cooling Crystallization

    22 Aug 2016 | Contributor(s):: Griffin, Daniel J.

    Advisors: Ronald W. Rousseau, Martha A. Grover, Yoshiaki Kawajiri, Matthew J. Realff, Bojan PetrovicIn taking a different view of crystallization dynamics, this thesis reveals a new framework for addressing a prevalent process engineering challenge: control over the size of crystals produced by...

  3. Almambet Iskakov

    Almambet is in his third year in the MINED group after obtaining his BS in Mechanical Engineering at University of Maryland, College Park. His research is focused on developing...


  4. Analysis of Fibrillar Structures for the Engineering of Polymeric Transistors

    22 Jan 2018 | Contributor(s):: Persson, Nils Erland

    Advisors: Martha A. Grover, Elsa Reichmanis, Carson Meredith, Joseph Schork, Jye-Chyi LuImage processing software was developed and applied to the analysis of polymer nanofiber microstructures in poly(3-hexylthiophene) (P3HT)-based organic field effect transistors. A large database of processing,...

  5. Andrew Marshall


  6. Andrew R Castillo


  7. Clustering and Feature Detection Methods for High-Dimensional Data

    08 Sep 2020 | Contributor(s):: Lahoti, Geet

    Advisors: Chuck Zhang, Kamran Paynabar, Jianjun Shi, Ben Wang, Zhen QianThe majority of the real-world data are unlabeled. Moreover, complex characteristics such as high-dimensionality and high variety pose significant analytical challenges. In statistical and machine learning, supervised and...

  8. Data-Driven Process-Structure-Property Models for Additive Manufactured Ni-Base Superalloys

    11 Jan 2021 | Contributor(s):: GorganNejad, Sanam

    Advisors: Richard Neu, David McDowell, Antonia Antoniou, Joshua Kacher, Kamran PaynabarThe complexity of the selective laser melting (SLM) process, which has shown success for shaping advanced structural alloys, has concentrated most of the research efforts to develop process-structure-property...

  9. Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures: codes and data files

    01 Feb 2018 | Contributor(s):: Noah H. Paulson

    This data-set contains the codes and data files relevant to the paper titled "Data-driven reduced-order models for rank-ordering thehigh cycle fatigue performance of polycrystalline microstructures" [Paulson2017_HomogenizationHCF]. Data-files are provided so that the figures...

  10. Flow-Assisted Erosion-Corrosion of Steel in Pulping Liquors Containing Particulates

    31 May 2018 | Contributor(s):: Baykal, Bedi Aydin

    Advisors: Preet M. Singh, Faisal M. Alamgir, Victor Breedveld, Hamid Garmestani, Arun M. GokhaleErosion-Corrosion is a type of corrosion where mechanical (erosive) and chemical (corrosive) effects combine to accelerate material loss due to corrosion. Erosion-corrosion can cause a higher rate of...

  11. 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...

  12. Michael DeFranks

    Leading the creation of Smart technology and integrated sleep technology platforms with keen focus on next generation materials and functional component design.


  13. MKS for Titanium Polycrystals - Rank Ordering of HCF Resistance using Localization


  14. MKS for Titanium Polycrystals - Predicting Bulk Properties


  15. Multilayer Perceptron (MLP) Neural Networks

    Collections | 18 Jul 2018 | Posted by Andrew Marshall


  16. 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...

  17. Nils Persson

    Nils E. Persson was born in Edina, Minnesota and grew up in nearby Minnetonka before moving to Minneapolis to pursue his B.S.E. in chemical engineering from the University of Minnesota. He is...


  18. Noah H. Paulson

    Noah Paulson is a 4th year PhD student at Georgia Tech developing computationally efficient protocols for the prediction of properties in polycrystalline materials. Noah grew up in Lexington,...


  19. Noah Paulson Research

    This wiki contains pages which introduce and motivate Noah Paulson’s Ph.D. work


  20. Noah Paulson Research - Background