Tags: machine learning

All Categories (1-20 of 129)

  1. 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.

  2. Machine Learning Framework for Impurity Level Prediction in Semiconductors

    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.

  3. Feature Selection for Machine Learning

    15 Dec 2020 | | Contributor(s):: Zachary D McClure, Alejandro Strachan

    Assessing feature selection for machine learning models

  4. 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.

  5. Nov 11 2020

    Machine Learning Framework for Impurity Level Prediction in Semiconductors workshop

    Register now: https://purdue.webex.com/purdue/onstage/g.php?MTID=e088a6ccfa042d4ac13bdb4450fa3d14bSpeaker: Dr. Arun Mannodi, Argonne National LaboratoryThis series of workshops introduces...

    https://nanohub.org/events/details/1875

  6. Only Physics can save Machine Learning!

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

  7. 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.

  8. 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...

  9. 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.

  10. Active Learning for Design of Experiments

    30 Sep 2020 | | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum

    This module introduces active learning in the context of materials discovery with hands-on online simulations.

  11. sayedul islam

    https://nanohub.org/members/301108

  12. 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. ...

  13. nanoHUB: Online Simulation and Data

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

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

  14. Ajjay S Gaadhe

    https://nanohub.org/members/300022

  15. Probabilistic Computing: From Materials and Devices to Circuits and Systems

    07 Sep 2020 | | Contributor(s):: Kerem Yunus Camsari

    In this talk, I will describe one such path based on the concept of probabilistic or p-bits that can be scalably built with present-day technology used in magnetic memory devices.

  16. Uncertainty Quantification and Scientific Machine Learning for Complex Engineering Systems

    17 Aug 2020 | | Contributor(s):: Guang Lin

    In this talk, I will first present a review of the novel UQ techniques I developed to conduct stochastic simulations for very large-scale complex systems.

  17. Ganesh Sri Sainath Chalamalasetti

    Professional with 3 years of experience in Product Optimization Engineering. Highly skilled in Solidworks, and engineering process flow includes existing and new product development. Currently...

    https://nanohub.org/members/296320

  18. ECG Data Analysis Using Machine Learning

    03 Aug 2020 | | Contributor(s):: Rebecca Mosier, Guang Lin

    Perform data analysis on ECG data using machine learning methods.

  19. Discovering discretized classical equations of motion using parsimonious neural networks

    09 Jul 2020 | | Contributor(s):: Saaketh Desai, Alejandro Strachan

    Design and train neural networks in conjunction with genetic algorithms to discover classical equations of motion in a discretized form

  20. High Temperature Oxide Property Explorer

    29 Jun 2020 | | Contributor(s):: Zachary D McClure, Alejandro Strachan

    Explore material properties of common and niche oxide materials for high-temperature applications