All Categories (1-20 of 241)

1. 3 min Research Talk: Deep Machine Learning for Machine Performance & Damage Prediction

   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...

2. 3 min Research Talk: Using Machine Learning for Materials Discovery and Property Prediction

   26 Sep 2019 | | Contributor(s):: Mackinzie S Farnell

   Machine Learning models present a transformative method of optimization and prediction in science and engineering research. In the chemical sciences, unsupervised deep learning models such as autoencoders have shown to be useful for property prediction and material...

3. 3 min Research Talk: Web-based Machine Learning Tool for Material Discovery and Property Prediction

   26 Sep 2019 | | Contributor(s):: Bryan Arciniega

   This model allows the end-user to increase their knowledge on a scarce data set by using a data-rich property set. We also investigate the effect of chemical representation and autoencoder type on property prediction and compound generation.

4. 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

5. May 26 2021

   A Hands-on Introduction to Physics-Informed Neural Networks

   Presenter:Ilias Bilionis, Purdue UniversityAbstract:Can you make a neural network satisfy a physical law? There are two main types of these laws: symmetries and ordinary/partial differential...

   https://nanohub.org/events/details/1980

6. 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...

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

   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...

8. 

   Abdelaali Fargi

   Abdelaali Fargi received his PhD in Physics of Semiconductor Devices and Electronics from Faculty of Sciences of Monastir (Tunisia) in 2016, the Master of Science Degree in Materials Science and...

   https://nanohub.org/members/56303

9. 

   Abdu-Jabbar Bozdar

   I graduated majoring in electronics and currently working as a Computer Programmer. My keen interest in electronics engineering, semiconductor materials and devices brought me here.

   https://nanohub.org/members/387719

10. Active Learning via Bayesian Optimization for Materials Discovery

    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.

11. Adaptations to Convection Cells

    21 Aug 2022 | | Contributor(s):: Chris Winkler, Rice University, NEWT Center

    Changing temperature differences between the poles and the equator, and the rate of the Earth’s spin, create unique atmospheric patterns. These movements help to transfer heat from the equator to the poles thus creating weather. Deep Learning is used to help predict the changes due to...

12. 

    Adedapo Sunday Adeyinka

    Adedapo Adeyinka is a Lecturer at the Department of Chemical Sciences, University of Johannesburg. He completed his PhD at the University of Pretoria and spent two years conducting Postdoctoral...

    https://nanohub.org/members/197673

13. Advancing Photonic Device Design and Quantum Measurements with Machine Learning

    18 Dec 2020 | | Contributor(s):: Alexandra Boltasseva

    In this talk, photonic design approaches and emerging material platforms will be discussed showcasting machine-learning-assisted topology optimization for thermophotovoltaic metasurface designs and machine-learning-enabled quantum optical measurements.

14. 

    Ahmed-Amine Homman

    https://nanohub.org/members/82074

15. 

    Ajjay S Gaadhe

    https://nanohub.org/members/300022

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

    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.

17. 

    Andrew Ferguson

    https://nanohub.org/members/84967

18. ANN Model Generator

    11 Jul 2022 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan

    Simtool workflow to create ANN models for user datasets

19. ANN-based friction factor and Nusselt number models for developing flow across square pin fins

    30 May 2023 | | Contributor(s):: Saeel Shrivallabh Pai, Justin A. Weibel

    ANN-based correlations which provide friction factor and Nusselt number values for developing flows across square pin fins of different pitch.

20. 

    Anoop A Nair

    I'm an integrated masters student in physics at the Indian Institute of Science Education and Research -Thiruvananthapuram

    https://nanohub.org/members/328484