-
A Guided Tour of Interactive Jupyter Notebooks Powered by nanoHUB
20 Feb 2023 | | Contributor(s):: Daniel Mejia
In this presentation, we will take you on a guided tour of interactive Jupyter Notebooks powered by nanoHUB. Jupyter is a powerful tool for data science and scientific computing that provides an intuitive interface for a variety of programming languages; Jupyter in nanoHUB provides even more...
-
A Hands-on Introduction to Physics-Informed Neural Networks
16 Jun 2021 | | Contributor(s):: Ilias Bilionis, Atharva Hans
Can you make a neural network satisfy a physical law? There are two main types of these laws: symmetries and ordinary/partial differential equations. I will focus on differential equations in this short presentation. The simplest way to bake information about a differential equation with neural...
-
A Hands-on Introduction to Physics-Informed Neural Networks
21 May 2021 | | Contributor(s):: Atharva Hans, Ilias Bilionis
A Hands-on Introduction to Physics-Informed Neural Networks
-
ANN Model Generator
11 Jul 2022 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
Simtool workflow to create ANN models for user datasets
-
Data Analysis with MATLAB
04 Mar 2022 | | Contributor(s):: Gen Sasaki
Learn how MATLAB can be used to visualize and analyze data, perform numerical computations, and develop algorithms. Through live demonstrations and examples, you will see how MATLAB can help you become more effective in your coursework as well as in research.
-
Data Cleaning with MATLAB
12 Oct 2022 | | Contributor(s):: Kelsey Joy Rodgers
This workshop will go over MATLAB built-in functions (readcell and writecell) to import data from Excel and export data to Excel.
-
Data Science and Machine Learning for Materials Science
22 Jan 2020 | | Contributor(s):: Saaketh Desai
This talk covers the fundamentals of machine learning and data science, focusing on material science applications. The talk is for a general audience, attempting to introduce basic concepts such as linear regression, supervised learning with neural networks including forward and back...
-
Data Science and Machine Learning for MSE Students: introduction and Hands-on Activities
10 Sep 2019 | | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum, Saaketh Desai
This document introduces basic concepts of data science and machine learning in the context of materials science applications. The focus is on hands-on activities where readers use open, online tools in nanoHUB to explore the concepts being introduced. Topics covered include querying data...
-
DFT Results Explorer
17 Feb 2021 | | Contributor(s):: Saaketh Desai, Juan Carlos Verduzco Gastelum, Daniel Mejia, Alejandro Strachan
Use visualization tools to explore correlations in a DFT simulation results database
-
Effective Integration of NIST Reference Data, Reference Materials, and Informatics in Support of Science and Technology
15 May 2019 | | Contributor(s):: Carlos A. Gonzalez
In this talk, a general description of NIST’s SRM program will be provided, highlighting some examples related to environmental science, clinical diagnoses and petroleum chemistry. In addition, issues related to the effective integration of reference data with reference materials and...
-
Hands-On Data Science and Machine Learning in Undergraduate Education
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.
-
Hands-on Data Science and Machine Learning Training Series
21 Apr 2020 | | Contributor(s):: Alejandro Strachan, Saaketh Desai, Arun Kumar Mannodi Kanakkithodi
his series of workshops introduces participants to important concepts and techniques in data science and machine learning in the context engineering and physical sciences applications. All workshops include hands-on activities.
-
Hands-on Deep Learning for Materials
10 Jun 2020 | | Contributor(s):: Saaketh Desai, Edward Kim, Vinay Hegde
This tool introduces users to deep learning techniques such as convolutional neural networks and variational auto encoders from a materials standpoint
-
Hands-on Deep Learning for Materials Science: Convolutional Networks and Variational Autoencoders
13 Nov 2020 | | Contributor(s):: Vinay Hegde, Alejandro Strachan
This tutorial introduces deep learning techniques such as convolutional neural networks and variational auto encoders from a materials standpoint.
-
Hands-on Sequential Learning and Design of Experiments
29 Apr 2020 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
This tutorial introduces the concept of sequential learning and information acquisition functions and how these algorithms can help reduce the number of experiments required to find an optimal candidate. A hands-on approach is presented to optimize the ionic conductivity of ceramic...
-
Introduction to dplyr, ggplot2 and Other tidyverse Friends: Modern Tools for Data Exploration and Visualization
08 Jul 2021 | | Contributor(s):: Rei Sanchez-Arias
In recent years, interest in the development of predictive models and the use of machine learning libraries has grown rapidly. As part of the efficient implementation of different models, a fundamental component of this process deals with data preparation and cleaning, followed by exploration,...
-
Introduction to Jupyter Notebooks, Data Organization and Plotting (1st offering)
21 Apr 2020 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
This tutorial gives an introductory demonstration of how to create and use Jupyter notebooks. It showcases the libraries Pandas to manipulate and organize data with functionalities similar to those of Excel on python, and Plotly, a library used to create interactive plots for enhanced...
-
Introduction to Jupyter Notebooks, Data Organization and Plotting (2nd offering)
21 Apr 2020 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
This tutorial gives an introductory demonstration of how to create and use Jupyter notebooks. It showcases the libraries Pandas to manipulate and organize data with functionalities similar to those of Excel on python, and Plotly, a library used to create interactive plots for enhanced...
-
Learning and Teaching Data Science using nanoHUB’s Cloud Resources
18 Mar 2022 | | Contributor(s):: Alejandro Strachan
This talk will discuss how data science is accelerating innovation in STEM fields. These tools enable the efficient handling of valuable data, the identification of patterns in large data collections, the development of predictive models, and the optimal design of experiments.
-
Machine Learning for Materials Science: Part 1
09 Feb 2019 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan, Saaketh Desai
Machine learning and data science tools applied to materials science