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Jupyter in nanoHUB: Developing and Deploying Jupyter Tools in nanoHUB
Online Presentations | 16 Dec 2020 | Contributor(s):: Alejandro Strachan
This presentation is available for pre-screening. The final presentation production will be forth coming.
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Hands-On Data Science and Machine Learning in Undergraduate Education
Courses | 07 Oct 2020 | Contributor(s):: Alejandro Strachan, Saaketh Desai, Juan Carlos Verduzco Gastelum, Michael N Sakano, Zachary D McClure, Joseph M. Cychosz, Jared Gray West
This series of modules introduce key concepts in data science in the context of application in materials science and engineering.
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Module 5: Neural Networks for Regression and Classification
Online Presentations | 01 Oct 2020 | Contributor(s):: Saaketh Desai, Alejandro Strachan
This module introduces neural networks for material science and engineering with hands-on online simulations. Neural networks are a subset of machine learning models used to learn mappings between inputs and outputs for a given dataset. Neural networks offer great flexibility and have shown...
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Module 4: Linear Regression Models
Online Presentations | 01 Oct 2020 | Contributor(s):: Michael N Sakano, Saaketh Desai, Alejandro Strachan
This module introduces linear regression in the context of materials science and engineering. We will apply liner regression to predict materials properties and to explore correlations between materials properties via hands-on online simulations. Linear regression is a supervised machine...
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nanoHUB: Online Simulation and Data
Presentation Materials | 24 Sep 2020 | Contributor(s):: Alejandro Strachan
These slides introduce nanoHUB, an open platform for online simulations and collaboration.
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COVID-19 data analysis
Tools | 07 Aug 2020 | Contributor(s):: Randy Heiland, Paul Macklin
Perform data analysis in a Jupyter notebook using data from the pc4covid19 tool.
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ECG Data Analysis Using Machine Learning
Tools | 03 Aug 2020 | Contributor(s):: Rebecca Mosier, Guang Lin
Perform data analysis on ECG data using machine learning methods.
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Interactive Learning Tools for Scientific Computing and Data Analysis Using R
Tools | 29 Jul 2020 | Contributor(s):: Cindy Nguyen, Rei Sanchez-Arias
Root-finding methods and numerical optimization techniques with applications in science, engineering, and data analysis
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Data Analysis of Normal Data Sets in Engineering
Tools | 24 Jul 2020 | Contributor(s):: Joseph Joshua Williams, Nancy Ruzycki
Statistical and data analysis concepts in engineering
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Matlab Data Analysis Using Jupyter Notebooks
Tools | 24 Jul 2020 | Contributor(s):: Jon Nykiel, Anna Leichty, Zachary D McClure, Alejandro Strachan, Aileen Ryan, Adrian Nat Gentry, Amanda Johnston, Tamara Jo Moore, Allen Garner, Peter Bermel
Use Jupyter Notebooks with a Matlab kernel running in the background for data analysis and intro to engineering homework problems
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Parsimonious neural networks
Tools | 09 Jul 2020 | Contributor(s):: Saaketh Desai, Alejandro Strachan
Design and train neural networks in conjunction with genetic algorithms to discover equations directly from data
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Refractory Complex Concentrated Alloy Melting Point Calculation
Tools | 28 May 2020 | Contributor(s):: Zachary D McClure, Saaketh Desai, Alejandro Strachan
Calculate melting point of BCC-type high entropy alloys through phase coexistence method
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Test Tool for Neural Network Reactive Force Field for CHNO systems
Tools | 14 May 2020 | Contributor(s):: Pilsun Yoo, Saaketh Desai, Michael N Sakano, Peilin Liao, Alejandro Strachan
Run molecular dynamics and Do testing using the neural network reactive force field for HE materials
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PhysiBoSSa: cell fate decision in TNF Boolean model
Tools | 13 May 2020 | Contributor(s):: Gerard Pradas, Arnau Montagud, Miguel Ponce de Leon
PhysiBoSSa model of the cell fate decision in TNF Boolean model in a multicellular multiscale system
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XRD interactive trends plot
Tools | 11 May 2020 | Contributor(s):: Enze Chen
Observe changes in powder XRD spectra by modifying experimental parameters.
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Running a Python 3 Script in a nanoHUB Jupyter Notebook
Online Presentations | 01 May 2020 | Contributor(s):: Tanya Faltens
This tutorial will show you how to create and run Python 3 code in a Jupyter notebook, rather than creating and running a Python script. We are working along with Chapter 1.8 “Writing a program” in the Python for Everybody course. In this lesson they execute a Python script that...
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Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (2nd offering)
Online Presentations | 30 Apr 2020 | Contributor(s):: Michael N Sakano, Alejandro Strachan
This tutorial introduces unsupervised machine learning algorithms through dimensionality reduction via matrix decomposition techniques in the context of chemical decomposition of reactive materials in a Jupyter notebook on nanoHUB.org. The tool used in this demonstration...
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Hands-on Supervised Learning: Part 2 - Classification and Random Forests (2nd offering)
Online Presentations | 30 Apr 2020 | Contributor(s):: Saaketh Desai
This tutorial introduces neural networks for classification tasks and random forests for regression tasks via Jupyter notebooks on nanoHUB.org. You will learn how to create and train a neural network to perform a classification, as well as how to define and train random forests. The tools used...
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Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (1st offering)
Online Presentations | 29 Apr 2020 | Contributor(s):: Michael N Sakano, Alejandro Strachan
This tutorial introduces unsupervised machine learning algorithms through dimensionality reduction via matrix decomposition techniques in the context of chemical decomposition of reactive materials in a Jupyter notebook on nanoHUB.org. The tool used in this demonstration...
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Hands-on Supervised Learning: Part 2 - Classification and Random Forests (1st offering)
Online Presentations | 24 Apr 2020 | Contributor(s):: Saaketh Desai
This tutorial introduces neural networks for classification tasks and random forests for regression tasks via Jupyter notebooks on nanoHUB.org. You will learn how to create and train a neural network to perform a classification, as well as how to define and train random forests. The tools used...