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Overview of Computational Methods and Machine Learning: Panel Discussion
Online Presentations | 14 Jun 2019 | Contributor(s):: Brett Matthew Savoie, Pradeep Kumar Gurunathan, Peilin Liao, Xiulin Ruan, Guang Lin
The individual Panel Talks which accompanies this discussion can be found here.Why do we need experiments?Are your methods “descriptive” or “predictive”?Do you work with any other theory/simulation groups?On the 5 year timescale: is machine-learning hype or a real...
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Overview of Computational Methods and Machine Learning: Panel Talks
Online Presentations | 14 Jun 2019 | Contributor(s):: Brett Matthew Savoie, Pradeep Kumar Gurunathan, Peilin Liao, Xiulin Ruan, Guang Lin
The Panel Discussion which follows these individual presentations can be found here.Individucal Presentations:Theory and Machine Learning in the Chemical Sciences, Brett Matthew Savoie;Divide and Conquer with QM/MM Methods, Pradeep Kumar Gurunathan;Computational Chemistry/Materials, Peilin...
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SMART Films Tutorials
Workshops | 05 Jun 2019 | Contributor(s):: Ali Shakouri (organizer)
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Big Data in Reliability and Security: Some Basics
Online Presentations | 30 May 2019 | Contributor(s):: Saurabh Bagchi
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Big Data in Reliability and Security: Applications
Online Presentations | 30 May 2019 | Contributor(s):: Saurabh Bagchi
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Human-Interpretable Concept Learning via Information Lattices
Online Presentations | 23 May 2019 | Contributor(s):: Lav R. Varshney
The basic idea is an iterative discovery algorithm that has a student-teacher architecture and that operates on a generalization of Shannon’s information lattice, which itself encodes a hierarchy of abstractions and is algorithmically constructed from group-theoretic foundations.
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Bryan Arciniega
Bryan Arciniega is a third year undergraduate at California State Polytechnic University, Pomona who is studying computer engineering and finance. He currently works as an IT technician for Cal...
https://nanohub.org/members/230182
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Nanomanufacturing with 2D Materials Informed by Machine Learning
Online Presentations | 22 Apr 2019 | Contributor(s):: Joel Ager
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Suprit Chaudhari
I am a final year undergraduate student of Engineering Physics at the Indian Institute of Technology (IIT), Guwahati. I am interested in Nanotechnology and machine learning.
https://nanohub.org/members/224852
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Literature transcriptomics review and data of Nanoparticle Induced Cellular Outcomes
Downloads | 07 Mar 2019 | Contributor(s):: Irini Furxhi
Data from in vitro differential gene expression analysis studies were gathered from peer-reviewed scientific literature. The studies gathered had a considerably variety of different human cell models including both primary cells and immortalized cell lines which exhibit varying...
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Machine Learning for Materials Science: Part 1
Tools | 09 Feb 2019 | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan, Saaketh Desai
Machine learning and data science tools applied to materials science
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3 min Research Talk: Deep Machine Learning for Machine Performance & Damage Prediction
Online Presentations | 04 Feb 2019 | Contributor(s):: Elijah Reber
In this talk, we look at how effective a deep neural network is at predicting the failure or energy output of a wind turbine. A data set was obtained that contained sensor data from 17 wind turbines over 13 months, measuring numerous variables, such as spindle speed and blade position and whether...
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Networked Dynamical Systems for Function and Learning: Paradigms for Data-Driven Control and Learning in Neurosensory Systems
Online Presentations | 16 Jan 2019 | Contributor(s):: J. Nathan Kutz
Our objective is to use emerging data-driven methods to extract the underlying engineering principles of cognitive capability, namely those that allow complex networks to learn and enact control and functionality in the robust manner observed in neurosensory systems. Mathematically, the...
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Data-Driven Discovery of Governing Equations of Physical Systems
Online Presentations | 16 Jan 2019 | Contributor(s):: J. Nathan Kutz
We introduce a number of data-driven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data. We consider two canonical cases: (i) systems for which we have full measurements of the governing variables, and (ii) systems for which we have incomplete...
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Creating Inflections: DARPA’s Electronics Resurgence Initiative
Online Presentations | 09 Jan 2019 | Contributor(s):: William Chappell
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ECE 695E: An Introduction to Data Analysis, Design of Experiment, and Machine Learning
Courses | 07 Jan 2019 | Contributor(s):: Muhammad A. Alam
This course will provide the conceptual foundation so that a student can use modern statistical concepts and tools to analyze data generated by experiments or numerical simulation.
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Imaging Sciences at the Oak Ridge National Laboratory: Identity Sciences, Advanced Manufacturing, Computational Imaging, Machine Learning, and Super Computing
Online Presentations | 03 Jan 2019 | Contributor(s):: Hector J. Santos-Villalobos
Dr. Santos takes us on the journey of working at the Oak Ridge National Laboratory as an imaging scientist. He showcases work in the areas of Identity Sciences (i.e., biometrics), Machine Learning, and Computational Imaging. Some application to discuss are coded source neutron imaging,...
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ME 498 Lecture 8: Quality Control in Today's Manufacturing
Online Presentations | 06 Dec 2018 | Contributor(s):: Chenhui Shao
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TensorFlow Tutorials
Tools | 03 Dec 2018 | Contributor(s):: Juan Carlos Verduzco Gastelum, Saaketh Desai, Alejandro Strachan
Ready-to-run Jupyter notebooks for machine learning using Tensorflow and Keras
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Desmond Brennan
Providing dissertation help at University of Florida
https://nanohub.org/members/214385