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The Materials Simulation Toolkit for Machine Learning (MAST-ML): Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction
06 Oct 2022 | | Contributor(s):: Ryan Jacobs
Hands-on activities, we will use MAST-ML to (1) import materials datasets from online databases and clean and examine our input data, (2) conduct feature engineering analysis, including generation, preprocessing, and selection of features, (3) construct, evaluate and compare the performance of...
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Aytekin Gel
https://nanohub.org/members/327168
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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.
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nanoHUB: Online Simulation and Data
24 Sep 2020 | | Contributor(s):: Alejandro Strachan
These slides introduce nanoHUB, an open platform for online simulations and collaboration.
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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.
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FunUQ for MD
22 Oct 2018 | | Contributor(s):: Sam Reeve, Alejandro Strachan
Functional uncertainty quantification for molecular dynamics
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Uncertainty Quantification Tutorial using Jupyter Notebooks
02 Oct 2018 | | Contributor(s):: Ilias Bilionis
Increasing modeling detail is not necessarily correlated with increasing predictive ability. Setting modeling and numerical discretization errors aside, the more detailed a model gets, the larger the number of parameters required to accurately specify its initial/boundary conditions,...
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ME 597UQ Lecture 24: Bayesian Model Comparison using Sequential Monte Carlo
27 Apr 2018 | | Contributor(s):: Ilias Bilionis
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ME 597UQ Lecture 20: Inverse Problems/Model Calibration - Bayesian Approach
30 Mar 2018 | | Contributor(s):: Ilias Bilionis
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ME 597UQ Uncertainty Quantification
02 Feb 2018 | | Contributor(s):: Ilias Bilionis
The goal of this course is to introduce the fundamentals of uncertainty quantification to advanced undergraduates or graduate engineering and science students with research interests in the field of predictive modeling.
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Quantifying Uncertainties in Physical Models
28 Aug 2017 | | Contributor(s):: Ilias Bilionis
Increasing modeling detail is not necessarily correlated with increasing predictive ability. Setting modeling and numerical discretization errors aside, the more detailed a model gets, the larger the number of parameters required to accurately specify its initial/boundary conditions, constitutive...
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Uncertainty Quantification in a Thermoelectric Device
Collections |
26 Oct 2016 |
Posted by Tanya Faltens
https://nanohub.org/groups/materials/collections/saved-materials-science-runs
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Uncertainty Quantification with Rappture
29 Sep 2016 | | Contributor(s):: Martin Hunt
Uncertainty Quantification (UQ) is a new feature in Rappture. This presentation discusses What is it? Why would you need it? How does it work? How do you use it?
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A Gentle Introduction to Uncertainty Quantification
19 Aug 2016 | | Contributor(s):: Ilias Bilionis
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Battery Optimization
27 Jul 2016 | | Contributor(s):: Lefei Zhang, Guang Lin, Salar Safarkhani
Tool for modeling Porous Lithium-Ion Batteries for optimization and uncertainty quantification.
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Uncertainty Quantification in Materials Modeling: Topics on Uncertainty Quantification
04 Dec 2015 | | Contributor(s):: Alejandro Strachan
This is the seminar portion of the NCN and NEEDS 2015 Summer School consisting of presentations related to uncertainty quantification.
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Efficient Exploration of Quantified Uncertainty in Granular Crystals
16 Mar 2016 | | Contributor(s):: Juan Camilo Lopez
This work presents a way of quantifying uncertainty in granular crystals in a computationally efficient way. To accomplish this, a low dimensional response surface is approximated through the method of active subspaces. Within this framework, special structure within the inputs is exploited to...
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Predictive Simulations of Materials and Devices with Quantified Uncertainties
06 Apr 2015 | | Contributor(s):: Alejandro Strachan
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High Dimensional Uncertainty Quantification via Multilevel Monte Carlo
02 Feb 2016 | | Contributor(s):: Hillary Fairbanks
Multilevel Monte Carlo (MLMC) has been shown to be a cost effective way to compute moments of desired quantities of interest in stochastic partial differential equations when the uncertainty in the data is high-dimensional. In this talk, we investigate the improved performance of MLMC versus...
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Multilevel Markov Chain Monte Carlo for Uncertainty Quantification in Subsurface Flow
02 Feb 2016 | | Contributor(s):: Christian Ketelsen
The multilevel Monte Carlo method has been shown to be an effective variance reduction technique for quantifying uncertainty in subsurface flow simulations when the random conductivity field can be represented by a simple prior distribution. In state-of-the-art subsurface simulation the...