
Parsimonious Neural Networks Learn Interpretable Physical Laws
21 Jun 2021   Contributor(s):: Saaketh Desai
Machine learning methods are widely used as surrogate models in the physical sciences, but less explored is the use of machine learning to discover interpretable laws from data. This tutorial introduces parsimonious neural networks (PNNs), a combination of neural networks and evolutionary...

A Handson Introduction to PhysicsInformed Neural Networks
16 Jun 2021   Contributor(s):: Ilias Bilionis, Atharva Hans
Can you make a neural network satisfy a physical law? There are two main types of these laws: symmetries and ordinary/partial differential equations. I will focus on differential equations in this short presentation. The simplest way to bake information about a differential equation with neural...

A Handson Introduction to PhysicsInformed Neural Networks
21 May 2021   Contributor(s):: Atharva Hans, Ilias Bilionis
A Handson Introduction to PhysicsInformed Neural Networks

Apr 23 2021
Parsimonious Neural Networks Learn Interpretable Physical Laws
Machine learning methods are widely used as surrogate models in the physical sciences, but less explored is the use of machine learning to discover interpretable laws from data. This tutorial...
https://nanohub.org/events/details/1974

Deep Learning for Time Series Illustrated by COVID19 Infection Studies
04 Feb 2021   Contributor(s):: Geoffrey C. Fox
We show that one can study several sets of sequences or timeseries in terms of an underlying evolution operator which can be learned with a deep learning network.

UNet Convolutional Neural Networks for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene
01 Feb 2021   Contributor(s):: Aagam Rajeev Shah
This tutorial introduces you to UNet, a popular convolutional neural network commonly developed for image segmentation in biomedicine. Using an assembled data set, you will learn how to create and train a UNet neural network, and apply it to segment scanning electron microscopy images of...

Module 5: Neural Networks for Regression and Classification
01 Oct 2020   Contributor(s):: Saaketh Desai, Alejandro Strachan
This module introduces neural networks for material science and engineering with handson online simulations. Neural networks are a subset of machine learning models used to learn mappings between inputs and outputs for a given dataset. Neural networks offer great flexibility and have shown great...

Machine Learning in Materials  Center for Advanced Energy Studies and Idaho National Laboratory
24 Sep 2020   Contributor(s):: Alejandro Strachan
his handson tutorial will introduce participants to modern tools to manage, organize, and visualize data as well as machine learning techniques to extract information from it. ...

05 Ferroelectric Devices for ComputeinMemory: ArrayLevel Operations
18 Sep 2020   Contributor(s):: Shimeng Yu, Panni Wang
Doped HfO2 based ferroelectric fieldeffect transistors (FeFETs) are being actively explored as emerging nonvolatile memory devices with the potential for computeinmemory (CIM) paradigm. In this work, we explored the feasibility of arraylevel operations of FeFET in the context of insitu...

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 largescale complex systems.

Parsimonious neural networks
09 Jul 2020   Contributor(s):: Saaketh Desai, Alejandro Strachan
Design and train neural networks in conjunction with genetic algorithms to discover equations directly from data

Stochastic Computing for Brainware LSI
29 Jun 2020 
This talk reviews stochastic computation and discusses the advantages and disadvantages with the recent developments in hardware. In addition, stochasticcomputing based brainware LSIs (BLSIs) are introduced.

SEM Image Segmentation Tutorial using SEM Image Processing Tool
16 Jun 2020   Contributor(s):: Joshua A Schiller
In this activity, students will learn about the use of image processing methods to analyze Scanning Electron Microscopy images using a technique known as Image Segmentation and the SEM Image Processing Tool. The purpose of this tutorial is demonstrate several methods for image masking:...

Parsimonious Neural Networks Learn Classical Mechanics and Can Teach It
15 May 2020   Contributor(s):: Saaketh Desai, Alejandro Strachan
We combine neural networks with genetic algorithms to find parsimonious models that describe the time evolution of a point particle subjected to an external potential. The genetic algorithm is designed to find the simplest, most interpretable network compatible with the training data. The...

Test Tool for Neural Network Reactive Force Field for CHNO systems
14 May 2020   Contributor(s):: Pilsun Yoo, Saaketh Desai, Michael N Sakano, Peilin Liao, Alejandro Strachan
Run molecular dynamics and Do testing using the neural network reactive force field for HE materials

Handson Supervised Learning: Part 2  Classification and Random Forests (2nd offering)
30 Apr 2020   Contributor(s):: Saaketh Desai
This tutorial introduces neural networks for classification tasks and random forests for regression tasks via Jupyter notebooks on nanoHUB.org. You will learn how to create and train a neural network to perform a classification, as well as how to define and train random forests. The tools used...

Image Segmentation for Graphene Images
29 Apr 2020   Contributor(s):: Joshua A Schiller
This lecture outlines the need for a fast, automated means for identifying regions of images corresponding to graphene. Simple methods, like color masking and template matching, are discussed initially. Unsupervised clustering methods are then introduced as potential improvements...

Handson Supervised Learning: Part 2  Classification and Random Forests (1st offering)
24 Apr 2020   Contributor(s):: Saaketh Desai
This tutorial introduces neural networks for classification tasks and random forests for regression tasks via Jupyter notebooks on nanoHUB.org. You will learn how to create and train a neural network to perform a classification, as well as how to define and train random forests. The tools used...

Handson Supervised Learning: Part 1  Linear Regression and Neural Networks
22 Apr 2020   Contributor(s):: Saaketh Desai
This tutorial introduces supervised learning via Jupyter notebooks on nanoHUB.org. You will learn how to setup a basic linear regression in a Jupyter notebook and then create and train a neural network. The tool used in this demonstration is Machine Learning for Materials Science:...

Handson Data Science and Machine Learning Training
21 Apr 2020   Contributor(s):: Alejandro Strachan, Saaketh Desai
This series of handson tutorials is designed to jump start your use of data science and machine learning in research or teaching. This series will cover the following topics: Learn how to use Jupyter notebooks for your research Interact with data repositories and manage...