Linear Regression Models
01 Oct 2020 | | Contributor(s):: Michael N Sakano, Saaketh Desai, Alejandro Strachan
This module introduces linear regression in the context of materials science and engineering.
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.
30 Jun 2020 | | Contributor(s):: Nano-Link Center for Nanotechnology Education, kim Grady, Rich Wilkosz (editor), Rodfal Alberto Rodriguez (editor), María Teresa Rivera (editor)
Este módulo le presenta a los estudiantes la nanotecnología, procesos y métodos que están siendo desarrollados para producir nano-fibras de celulosa fuertes y rígidas. El módulo está diseñado para guiar a los estudiantes a...
Digital Materials Design
11 May 2020 | | Contributor(s):: Mark Knackstedt
The key to DMD is access to efficient facilities and tools to characterisematerial structure and function at multiple scales (from nanometers to structural sizes), in multiple states (relaxed vs under compression, before/after reaction or dissolution) and with multiple probes (multiple x-ray...
Hands-on Sequential Learning and Design of Experiments
29 Apr 2020 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
This tutorial introduces the concept of sequential learning and information acquisition functions and how these algorithms can help reduce the number of experiments required to find an optimal candidate. A hands-on approach is presented to optimize the ionic conductivity of ceramic...
05 Feb 2020 | | Contributor(s):: kim Grady, Richard Wilkosz (editor), Nano-Link Center for Nanotechnology Education
This module presents the nanotechnologies, processes, and methods being developed to produce strong and stiff cellulose nanofibers. The module is designed to take the student through processing a material (called feed stock or substrate) to break down and...
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
Use of RBT unreleased NEM resonator models for device (topological) optimization
26 Apr 2017 | | Contributor(s):: Bichoy W. Bahr
The use of Phononic Crystals (PnC) in suspended structures and microstructures, such as plates and slabs, has gained a lot of attention in the past years for the wide range of feasible applications (acoustic waveguides, acoustic insulation, acoustic cloaking) and for the easy fabrication...
nanoHUB Materials Simulation Homework: Engineering the Yield Stress of a Material by Controlling Grain Size
26 Feb 2015 | | Contributor(s):: Marisol Koslowski, Tanya Faltens
This homework assignment uses the nanoplasticity lab simulation tool to enable students to explore how grain size and the competing plastic deformation mechanisms of dislocation motion and grain boundary sliding affect the yield stress of a sample. Students create conditions that lead to...
11 Oct 2007 | | Contributor(s):: Stephen Langer, R. Edwin García, Andrew Reid
Object oriented finite element analysis tool