Tags: Machine Learning

All Categories (1-20 of 49)

  1. Overview of Computational Methods and Machine Learning: Panel Discussion

    14 Jun 2019 | | Contributor(s):: Brett Matthew Savoie, Pradeep Kumar Gurunathan, Peilin Liao, Xiulin Ruan, Guang Lin

    The individual Panel Talks which accompanies this discussion can be found here.Why do we need experiments?Are your methods “descriptive” or “predictive”?Do you work with any other theory/simulation groups?On the 5 year timescale: is machine-learning hype or a real...

  2. Overview of Computational Methods and Machine Learning: Panel Talks

    14 Jun 2019 | | Contributor(s):: Brett Matthew Savoie, Pradeep Kumar Gurunathan, Peilin Liao, Xiulin Ruan, Guang Lin

    The Panel Discussion which follows these individual presentations can be found here.Individucal Presentations:Theory and Machine Learning in the Chemical Sciences, Brett Matthew Savoie;Divide and Conquer with QM/MM Methods, Pradeep Kumar Gurunathan;Computational Chemistry/Materials, Peilin...

  3. SMART Films Tutorials

    05 Jun 2019 | | Contributor(s):: Ali Shakouri (organizer)

  4. Big Data in Reliability and Security: Some Basics

    30 May 2019 | | Contributor(s):: Saurabh Bagchi

  5. Big Data in Reliability and Security: Applications

    30 May 2019 | | Contributor(s):: Saurabh Bagchi

  6. Human-Interpretable Concept Learning via Information Lattices

    23 May 2019 | | Contributor(s):: Lav R. Varshney

    The basic idea is an iterative discovery algorithm that has a student-teacher architecture and that operates on a generalization of Shannon’s information lattice, which itself encodes a hierarchy of abstractions and is algorithmically constructed from group-theoretic foundations.

  7. Bryan Arciniega

    Bryan Arciniega is a third year undergraduate at California State Polytechnic University, Pomona who is studying computer engineering and finance. He currently works as an IT technician for Cal...

    https://nanohub.org/members/230182

  8. Nanomanufacturing with 2D Materials Informed by Machine Learning

    22 Apr 2019 | | Contributor(s):: Joel Ager

  9. Suprit Chaudhari

    I am a final year undergraduate student of Engineering Physics at the Indian Institute of Technology (IIT), Guwahati. I am interested in Nanotechnology and machine learning.

    https://nanohub.org/members/224852

  10. Literature transcriptomics review and data of Nanoparticle Induced Cellular Outcomes

    07 Mar 2019 | | Contributor(s):: Irini Furxhi

    Data from in vitro differential gene expression analysis studies were gathered from peer-reviewed scientific literature. The studies gathered had a considerably variety of different human cell models including both primary cells and immortalized cell lines which exhibit varying...

  11. Machine Learning for Materials Science: Part 1

    09 Feb 2019 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan, Saaketh Desai

    Machine learning and data science tools applied to materials science

  12. 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...

  13. Networked Dynamical Systems for Function and Learning: Paradigms for Data-Driven Control and Learning in Neurosensory Systems

    16 Jan 2019 | | Contributor(s):: J. Nathan Kutz

    Our objective is to use emerging data-driven methods to extract the underlying engineering principles of cognitive capability, namely those that allow complex networks to learn and enact control and functionality in the robust manner observed in neurosensory systems. Mathematically, the...

  14. Data-Driven Discovery of Governing Equations of Physical Systems

    16 Jan 2019 | | Contributor(s):: J. Nathan Kutz

    We introduce a number of data-driven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data. We consider two canonical cases: (i) systems for which we have full measurements of the governing variables, and (ii) systems for which we have incomplete...

  15. Creating Inflections: DARPA’s Electronics Resurgence Initiative

    09 Jan 2019 | | Contributor(s):: William Chappell

  16. ECE 695E: An Introduction to Data Analysis, Design of Experiment, and Machine Learning

    07 Jan 2019 | | Contributor(s):: Muhammad A. Alam

    This course will provide the conceptual foundation so that a student can use modern statistical concepts and tools to analyze data generated by experiments or numerical simulation.

  17. TensorFlow Tutorials

    03 Dec 2018 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Saaketh Desai, Alejandro Strachan

    Ready-to-run Jupyter notebooks for machine learning using Tensorflow and Keras

  18. Desmond Brennan

    Providing dissertation help at University of Florida

    https://nanohub.org/members/214385

  19. Juan Carlos Verduzco Gastelum

    Materials Engineering PhD Graduate Student at Purdue University.Research in "Solid-state energy storage devices rational materials design".Background in Mechanical and Electrical Engineering.

    https://nanohub.org/members/207041

  20. Deep Machine Learning for Machine Performance and Damage Prediction

    08 Aug 2018 | | Contributor(s):: Elijah Reber, Nickolas D Winovich, Guang Lin

    Deep learning has provided opportunities for advancement in many fields. One such opportunity is being able to accurately predict real world events. Ensuring proper motor function and being able to predict energy output is a valuable asset for owners of wind turbines. In this paper, we look at...