Physics-informed neural networks

Neural Network Overview

This lecture gives an overview of neural networks, which play an important role in machine learning today. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com

From playlist Intro to Data Science

Neural Networks and Deep Learning

This lecture explores the recent explosion of interest in neural networks and deep learning in the context of 1) vast and increasing data sets, and 2) rapidly improving computational hardware, which have enabled the training of deep neural networks. Book website: http://databookuw.com/

From playlist Intro to Data Science

Neural Network Architectures & Deep Learning

This video describes the variety of neural network architectures available to solve various problems in science ad engineering. Examples include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. Book website: http://databookuw.com/ Steve Brunton

From playlist Data Science

Neural Networks: Caveats

This lecture discusses some key limitations of neural networks and suggests avenues of ongoing development. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com

From playlist Intro to Data Science

Neural networks [9.6] : Computer vision - convolutional network

From playlist Neural networks class - Université de Sherbrooke

Neural Networks 1 Neural Units

From playlist Week 5: Neural Networks

What is Neural Network in Machine Learning | Neural Network Explained | Neural Network | Simplilearn

This video by Simplilearn is based on Neural Networks in Machine Learning. This Neural Network in Machine Learning Tutorial will cover the fundamentals of Neural Networks along with theoretical and practical demonstrations for a better learning experience 🔥Enroll for Free Machine Learning

From playlist Machine Learning Algorithms [2022 Updated]

Neural Network Fundamentals (Part 4): Prediction

From http://www.heatonresearch.com. In this part we see how to present data to a neural network to predict data.

From playlist Neural Networks by Jeff Heaton

Neural Networks (Part 1)

In this video, I present some applications of artificial neural networks and describe how such networks are typically structured. My hope is to create another video (soon) in which I describe how neural networks are actually trained from data.

From playlist Machine Learning

DDPS | "When and why physics-informed neural networks fail to train" by Paris Perdikaris

Physics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial differential equations. However, despite their noticeable empirical success, little is known about how such c

From playlist Data-driven Physical Simulations (DDPS) Seminar Series

Discovering Symbolic Models from Deep Learning with Inductive Biases (Paper Explained)

Neural networks are very good at predicting systems' numerical outputs, but not very good at deriving the discrete symbolic equations that govern many physical systems. This paper combines Graph Networks with symbolic regression and shows that the strong inductive biases of these models ca

From playlist Papers Explained

DDPS | A mathematical understanding of modern Machine Learning: theory, algorithms and applications

In this talk from July 15, 2021, Brown University assistant professor Yeonjong Shin discusses the development of robust and reliable machine learning algorithms based on insights gained from the mathematical analysis. Description: Modern machine learning (ML) has achieved unprecedented em

From playlist Data-driven Physical Simulations (DDPS) Seminar Series

Stanford Seminar - Computing with Physical Systems

Peter McMahon, Cornell University June 1, 2022 With conventional digital computing technology reaching its limits, there has been a renaissance in analog computing across a wide range of physical substrates. In this talk I will introduce the concept of Physical Neural Networks [1] and des

From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series

Physics-Informed Machine Learning: Blending data and physics for fast predictions

Dr. George Em Karniadakis, The Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering, Brown University presenting at our SSMCDAT Hackathon webinar.

From playlist Materials Informatics

Frank Noé: "Intro to Machine Learning (Part 2/2)"

Watch part 1/2 here: https://youtu.be/PzyhtPtrhs0 Machine Learning for Physics and the Physics of Learning Tutorials 2019 "Intro to Machine Learning (Part 2/2)" Frank Noé, Freie Universität Berlin Institute for Pure and Applied Mathematics, UCLA September 6, 2019 For more information:

From playlist Machine Learning for Physics and the Physics of Learning 2019

Deep Learning and Computations of PDEs by Siddhartha Mishra

COLLOQUIUM DEEP LEARNING AND COMPUTATIONS OF PDES SPEAKER: Siddhartha Mishra (Professor of Applied Mathematics, ETH Zürich, Switzerland) DATE & TIME: Mon, 27 June 2022, 15:30 to 17:00 VENUE: Online Colloquium ABSTRACT Partial Differential Equations (PDEs) are ubiquitous in the scien

From playlist ICTS Colloquia

Paris Perdikaris: "Overcoming gradient pathologies in constrained neural networks"

Machine Learning for Physics and the Physics of Learning 2019 Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature "Overcoming gradient pathologies in constrained neural networks" Paris Perdikaris - University of Penns

From playlist Machine Learning for Physics and the Physics of Learning 2019

DIRECT 2021 12 Scientific Machine Learning

DIRECT Consortium at The University of Texas at Austin, working on novel methods and workflows in spatial, subsurface data analytics, geostatistics and machine learning. This is Applications of Scientific Machine Learning for Petroleum Engineering. Join the consortium for access to all

From playlist DIRECT Consortium, The University of Texas at Austin

A Hands-on Introduction to Physics-informed Machine Learning

2021.05.26 Ilias Bilionis, Atharva Hans, Purdue University Table of Contents below. This video is part of NCN's Hands-on Data Science and Machine Learning Training Series which can be found at: https://nanohub.org/groups/ml/handsontraining Can you make a neural network satisfy a physical

From playlist ML & Deep Learning

DDPS | Competitive Physics Informed Networks by Spencer Bryngelson

Description: Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE residual as the loss function. This strategy is called "physics-informed neural networks" (PINNs), but it currently cannot produce high-accuracy solutions, typically attaining about

From playlist Data-driven Physical Simulations (DDPS) Seminar Series