All Categories (1-17 of 17)

1. Gaussian Process Regression for Surface Interpolation

   Online Presentations | 22 Nov 2022 | Contributor(s):: Zhiqiao Dong, Manan Mehta

   This tutorial will introduce the fundamentals of GPR and its application to surface interpolation. We will also introduce a new technique called filtered kriging (FK), which uses a pre-filter to improve interpolation performance.

2. No-code ML models

   Tools | 18 Oct 2022 | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan

   No-code ML models

3. Machine Learning Predicts Additive Manufacturing Part Quality: Tutorial on Support Vector Regression

   Online Presentations | 26 Aug 2022 | Contributor(s):: Davis McGregor

   This tutorial introduces and demonstrates the use of machine learning (ML) to address this need. Using data collected from an AM factory, you will train a support vector regression (SVR) model to predict the dimensions of AM parts based on the design geometry and manufacturing parameters.

4. SVR Machine Learning Workshop

   Tools | 08 Aug 2022 | Contributor(s):: Davis McGregor

   Introductory tutorial on support vector regression (SVR) machine learning, cross validation, and hyperparameter tuning.

5. A Machine Learning Aided Hierarchical Screening Strategy for Materials Discovery

   Online Presentations | 09 Sep 2021 | Contributor(s):: Anjana Talapatra

   In this tutorial, we illustrate this approach using the example of wide band gap oxide perovskites. We will sequentially search a very large domain space of single and double oxide perovskites to identify candidates that are likely to be formable, thermodynamically stable, exhibit insulator...

6. Active Learning via Bayesian Optimization for Materials Discovery

   Online Presentations | 25 Jun 2021 | Contributor(s):: Hieu Doan, Garvit Agarwal

   In this tutorial, we will demonstrate the use of active learning via Bayesian optimization (BO) to identify ideal molecular candidates for an energy storage application.

7. An Introduction to Machine Learning for Materials Science: A Basic Workflow for Predicting Materials Properties

   Online Presentations | 25 Jun 2021 | Contributor(s):: Benjamin Afflerbach

   This tutorial will introduce core concepts of machine learning through the lens of a basic workflow to predict material bandgaps from material compositions.

8. Bayesian optimization tutorial using Jupyter notebook

   Tools | 11 Jun 2021 | Contributor(s):: Hieu Doan, Garvit Agarwal

   Active learning via Bayesian optimization for materials discovery

9. Machine Learning Framework for Impurity Level Prediction in Semiconductors

   Online Presentations | 15 Dec 2020 | Contributor(s):: Arun Kumar Mannodi Kanakkithodi

   In this work, we perform screening of functional atomic impurities in Cd-chalcogenide semiconductors using high-throughput computations and machine learning.

10. Machine Learning Defect Behavior in Semiconductors

    Tools | 10 Nov 2020 | Contributor(s):: Arun Kumar Mannodi Kanakkithodi, Rushik Desai (editor)

    Develop machine learning models to predict defect formation energies in chalcogenides

11. 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.

12. Apr 14 2020

    Supervised learning part 1: linear regression and neural networks

    Topics covered in this session:Simple regressionDeveloping and training neural networksOrganizers: Alejandro Strachan, Saaketh DesaiLeader: Saaketh DesaiRegister for this seminarHands-on data...

    https://nanohub.org/events/details/1848

13. Apr 13 2020

    Supervised learning part 1: linear regression and neural networks

    Topics covered in this session:Simple regressionDeveloping and training neural networksOrganizers: Alejandro Strachan, Saaketh DesaiLeader: Saaketh DesaiRegister for this seminarHands-on data...

    https://nanohub.org/events/details/1842

14. Cluster Optimization BGO 01

    Tools | 29 Jul 2015 | Contributor(s):: Ilias Bilionis, Yinuo Li

    Cluster Optimization Using Bayesian Global Optimization

15. Gaussian process regression in 1D

    Tools | 26 Nov 2014 | Contributor(s):: Ilias Bilionis, Alejandro Strachan, Benjamin P Haley, Martin Hunt, Rohit Kaushal Tripathy, Sam Reeve

    Use Gaussian processes to represent x-y data

16. Bayesian Calibration

    Tools | 18 Feb 2014 | Contributor(s):: Martin Hunt, Benjamin P Haley, Jan Ebinger, Alejandro Strachan

    Given a model, input data for some paramaters and output data, calibrate unknown input parameters

17. IMA 2013 UQ: Bayesian Approaches for Spatial- Stochastic Basis Selection: Applications to Fuel Cell Predictive Modeling

    Online Presentations | 13 Mar 2014 | Contributor(s):: Guang Lin

    In this talk, two fully Bayesian methods (Bayesian uncertainty method and Bayesian mixture procedure) will be introduced that can evaluate generalized Polynomial Chaos (gPC) expansions in both stochastic and spatial domains when the number of the available basis functions is significantly larger...