Tags: data science

All Categories (1-20 of 39)

  1. Sheimy Paz Serpa

    https://nanohub.org/members/330719

  2. 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

  3. Materials Simulation Toolkit for Machine Learning (MAST-ML) tutorial

    07 May 2021 | Contributor(s):: Ryan Jacobs, BENJAMIN AFFLERBACH

    Tutorial showing the many use cases for the MAST-ML package to build, evaluate and analyze machine learning models for materials applications.

  4. 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

  5. Module 1: Making Data Accessible, Discoverable and Useful

    27 Jan 2021 | | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum

    This module focuses on the importance of make materials data discoverable, interoperable, and available and best practices to doing so. Data generation is both time consuming and costly, thus, making the available, as appropriate, with the community is critical to accelerate innovation. This is...

  6. Module 3: Materials Descriptors for Data Science

    27 Jan 2021 | | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum, Zachary D McClure

    This module focuses on the use of descriptors to improve the description of materials in machine learning. Augmenting input parameters with appropriate descriptors (a process sometimes called featurization) can often significantly improve the accuracy of predictive models. Ideal descriptors are...

  7. 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.

  8. Oct 21 2020

    Hands-on Deep Learning for Materials Science: Convolutional Networks and Variational Autoencoders workshop

    Registration for this event is now closed. Thanks for your interest!This series of workshops introduces participants to important concepts and techniques in data science and machine learning in the...

    https://nanohub.org/events/details/1869

  9. Only Physics can save Machine Learning!

    13 Oct 2020 | | Contributor(s):: Muhammad A. Alam

  10. 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.

  11. Module 5: Neural Networks for Regression and Classification

    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 great...

  12. Module 4: Linear Regression Models

    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 learning...

  13. Module 2: Querying Materials Data Repositories

    30 Sep 2020 | | Contributor(s):: Zachary D McClure, Alejandro Strachan

    This module introduces modern tools for data acquisition, including performing large queries using application programming interfaces (APIs), with hands-on online workflows. Cyber-infrastructure platforms for data offer unparalleled access to data, this module will introduce tools to manage,...

  14. Machine Learning in Materials - Center for Advanced Energy Studies and Idaho National Laboratory

    24 Sep 2020 | | Contributor(s):: Alejandro Strachan

    his hands-on tutorial will introduce participants to modern tools to manage, organize, and visualize data as well as machine learning techniques to extract information from it. ...

  15. nanoHUB: Online Simulation and Data

    24 Sep 2020 | | Contributor(s):: Alejandro Strachan

    These slides introduce nanoHUB, an open platform for online simulations and collaboration.

  16. Portrait of a Black Hole & Beyond

    26 Aug 2020 | | Contributor(s):: Katie L. Bouman

    Dr. Bouman, who was part of the Event Horizon Telescope team that captured the first photograph of a black hole, will talk about the challenges of the project.

  17. Katie L. Bouman

    Katie Bouman is a Rosenberg Scholar and an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. Before joining Caltech, she was a...

    https://nanohub.org/members/298104

  18. Jalil Reed

    M Jalil Reed is a 4th year civil engineering and mathematics student at Florida A&M University and he is interested in data science research and transportation engineering.

    https://nanohub.org/members/296553

  19. Hae Ji Kwon

    Hae Ji Kwon received AS in Engineering from Ivy Tech Community College. She is pursuing a BS in Engineering at Purdue University starting Fall 2020. Her interest is in Mechanical Engineering,...

    https://nanohub.org/members/288457

  20. Rebecca Mosier

    Rebecca Mosier is a second-year undergraduate student at Johns Hopkins University. Her majors are Biomedical Engineering and Applied Mathematics & Statistics. She is working on the Data-Driven...

    https://nanohub.org/members/288446