Tags: neural networks

Description

Neural networks are computing systems vaguely inspired by biological neural networks that as found in human or animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules.

All Categories (1-20 of 51)

  1. 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...

  2. A Hands-on Introduction to Physics-Informed 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...

  3. A Hands-on Introduction to Physics-Informed Neural Networks

    21 May 2021 | | Contributor(s):: Atharva Hans, Ilias Bilionis

    A Hands-on Introduction to Physics-Informed Neural Networks

  4. 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

  5. Deep Learning for Time Series Illustrated by COVID-19 Infection Studies

    04 Feb 2021 | | Contributor(s):: Geoffrey C. Fox

    We show that one can study several sets of sequences or time-series in terms of an underlying evolution operator which can be learned with a deep learning network.

  6. U-Net 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 U-Net, 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 U-Net neural network, and apply it to segment scanning electron microscopy images of...

  7. 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 hands-on 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...

  8. Machine Learning in Materials - Center for Advanced Energy Studies and Idaho National Laboratory

    24 Sep 2020 | | Contributor(s):: Alejandro Strachan

    his hands-on tutorial will introduce participants to modern tools to manage, organize, and visualize data as well as machine learning techniques to extract information from it. ...

  9. 05 Ferroelectric Devices for Compute-in-Memory: Array-Level Operations

    18 Sep 2020 | | Contributor(s):: Shimeng Yu, Panni Wang

    Doped HfO2 based ferroelectric field-effect transistors (FeFETs) are being actively explored as emerging nonvolatile memory devices with the potential for compute-in-memory (CIM) paradigm. In this work, we explored the feasibility of array-level operations of FeFET in the context of in-situ...

  10. 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.

  11. 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

  12. 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, stochastic-computing based brainware LSIs (BLSIs) are introduced.

  13. 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:...

  14. 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...

  15. 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

  16. Hands-on 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...

  17. 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...

  18. Hands-on 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...

  19. Hands-on 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:...

  20. Hands-on Data Science and Machine Learning Training

    21 Apr 2020 | | Contributor(s):: Alejandro Strachan, Saaketh Desai

    This series of hands-on 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...