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  1. Accelerating Radiation Damage Simulation Through Machine Learning

    Online Presentations | 21 May 2024 | Contributor(s):: Vinay Gupta, Shrienidhi Gopalakrishnan, Brian Hyun-jong Lee, Alejandro Strachan

    This study explores the challenge of material degradation from radiation exposure, a phenomenon that significantly impacts fields ranging from materials science to nuclear engineering and space exploration. As of today, the primary solution of conventional simulation techniques are...

  2. Eugenio Culurciello

    https://culurciello.github.iohttps://euge-blog.github.io

    https://nanohub.org/members/403223

  3. Adaptations to Convection Cells

    Animations | 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...

  4. Chemprop Demo

    Tools | 11 Apr 2022 | Contributor(s):: Kevin Greenman

    Demo of the Chemprop message-passing neural network package for the Hands-on Data Science and Machine Learning Training Series

  5. MATLAB R2021a

    Tools | 09 Sep 2021 | Contributor(s):: Gen Sasaki, Lisa Kempler

    MATLAB is a programming and numeric computing platform to analyze data, develop algorithms, and create models.

  6. Debugging Neural Networks

    Online Presentations | 09 Sep 2021 | Contributor(s):: Rishi P Gurnani

    The presentation will start with an overview of deep learning theory to motivate the logic in NetDebugger and end with a hands-on NetDebugger tutorial involving PyTorch, RDKit, and polymer data

  7. Debugging Neural Networks

    Tools | 07 Aug 2021 | Contributor(s):: Rishi P Gurnani

    Debug common errors in neural networks.

  8. Aytekin Gel

    https://nanohub.org/members/327168

  9. Qazi Shahid Ullah

    https://nanohub.org/members/322959

  10. Mar 30 2021

    MNT-EC Spring Development Workshop: CVD Synthesis and Image Analysis

    This hands-on tutorial will introduce users to the Gr-ResQ ('graphene rescue') platform. Gr-ResQ is (i) an open, crowd-sourced database of recipes and characterization of graphene...

    https://nanohub.org/events/details/1966

  11. Mar 23 2021

    MNT-EC Spring Development Workshop: CVD Synthesis and Image Analysis

    This hands-on tutorial will introduce users to the Gr-ResQ ('graphene rescue') platform. Gr-ResQ is (i) an open, crowd-sourced database of recipes and characterization of graphene...

    https://nanohub.org/events/details/1965

  12. Deep Learning for Time Series Illustrated by COVID-19 Infection Studies

    Online Presentations | 04 Feb 2021 | Contributor(s):: Geoffrey C. Fox

    We show that one can study several sets of sequences or time-series in terms of an underlying evolution operator which can be learned with a deep learning network.

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

    Online Presentations | 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. Hands-on Deep Learning for Materials Science: Convolutional Networks and Variational Autoencoders

    Online Presentations | 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.

  15. Evren Toptop

    https://nanohub.org/members/303331

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

    Online Presentations | 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. Hands-on Deep Learning for Materials

    Tools | 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

  19. Nathan Killoran

    Nathan holds a MSc in Mathematics from the University of Toronto and a PhD in Physics from the University of Waterloo. He specializes in quantum computing, deep learning, and quantum optics.

    https://nanohub.org/members/286348

  20. Aydogan Ozcan

    Dr. Ozcan is the Chancellor’s Professor and the Volgenau Chair for Engineering Innovation at UCLA and an HHMI Professor with the Howard Hughes Medical Institute, leading the Bio- and Nano-Photonics...

    https://nanohub.org/members/277073