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...
Overview of Computational Methods and Machine Learning: Panel Talks
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...
SMART Films Tutorials
05 Jun 2019 | | Contributor(s):: Ali Shakouri (organizer)
Big Data in Reliability and Security: Some Basics
30 May 2019 | | Contributor(s):: Saurabh Bagchi
Big Data in Reliability and Security: Applications
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.
Nanomanufacturing with 2D Materials Informed by Machine Learning
22 Apr 2019 | | Contributor(s):: Joel Ager
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...
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
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...
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...
Data-Driven Discovery of Governing Equations of Physical Systems
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...
Creating Inflections: DARPA’s Electronics Resurgence Initiative
09 Jan 2019 | | Contributor(s):: William Chappell
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.
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
Juan Carlos Verduzco Gastelum
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...