11_7_1 Potential Function of a Vector Field Part 1
The gradient of a function is a vector. n-Dimensional space can be filled up with countless vectors as values as inserted into a gradient function. This is then referred to as a vector field. Some vector fields have potential functions. In this video we start to look at how to calculat
From playlist Advanced Calculus / Multivariable Calculus
Introduction to Vector Fields This video discusses, 1) The definition of a vector field. 2) Examples of vector fields including the gradient, and various velocity fields. 3) The definition of a conservative vector field. 4) The definition of a potential function. 5) Test for conservative
From playlist Calculus 3
Linear Algebra for Computer Scientists. 1. Introducing Vectors
This computer science video is one of a series on linear algebra for computer scientists. This video introduces the concept of a vector. A vector is essentially a list of numbers that can be represented with an array or a function. Vectors are used for data analysis in a wide range of f
From playlist Linear Algebra for Computer Scientists
Intro to VECTOR FIELDS // Sketching by hand & with computers
Vector Fields are extremely important in math, physics, engineering, and many other fields. Gravitational fields, electric fields, magnetic fields, velocity fields, these are all examples of vector fields. In this video we will define the concept of a vector field, talk about some basic te
Free ebook http://tinyurl.com/EngMathYT Basic example of how to sketch a constant vector field.
From playlist Engineering Mathematics
One prominent example of a vector field is the Gradient Vector Field. Given any scalar, multivariable function f: R^n\to R, we can get a corresponding vector field that has a precise geometrical meaning: the vectors point in the direction of maximal increase of the function. MY VECTOR CA
After our introduction to matrices and vectors and our first deeper dive into matrices, it is time for us to start the deeper dive into vectors. Vector spaces can be vectors, matrices, and even function. In this video I talk about vector spaces, subspaces, and the porperties of vector sp
From playlist Introducing linear algebra
Welcome to the replacement theorem, which is *the* theorem that makes linear algebra work. Intuitively it says that any linearly independent set can be extended to be a spanning set. In this video, I state the replacement theorem and show some cool consequences. For example, using this the
From playlist Vector Spaces
Yusu Wang (4/25/18): Graph reconstruction via discrete Morse theory
Graphs form one of the most important types of data in various applications across science and engineering. They could be geometric in nature, such as road networks in GIS, or relational and abstract, such as protein-protein interaction networks. A fundamental problem is to reconstruct a h
From playlist AATRN 2018
Calculus 3 Lecture 15.1: INTRODUCTION to Vector Fields (and what makes them Conservative)
Calculus 3 Lecture 15.1: INTRODUCTION to Vector Fields (and what makes them Conservative): What Vector Fields are, and what they look like. We discuss graphing Vector Fields in 2-D and 3-D and talk about what a Conservative Vector Field means.
From playlist Calculus 3 (Full Length Videos)
Measurements vs. Bits: Compressed Sensors and Info Theory
October 18, 2006 lecture by Dror Baron for the Stanford University Computer Systems Colloquium (EE 380). Dror Baron discusses the numerous rich insights information theory has to offer Compressed Sensing (CS), an emerging field based on the revelation that optimization routines can reco
From playlist Course | Computer Systems Laboratory Colloquium (2006-2007)
DDPS | Data-driven methods for fluid simulations in computer graphics
Fluid phenomena are ubiquitous to our world experience: winds swooshing through trembling leaves, turbulent water streams running down a river, and cellular patterns generated from wrinkled flames are some few examples. These complex phenomena capture our attention and awe due to the beaut
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Lecture 12/16 : Restricted Boltzmann machines (RBMs)
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 12A The Boltzmann Machine learning algorithm 12B More efficient ways to get the statistics 12C Restricted Boltzmann Machines 12D An example of Contrastive Divergence Learning 12E RBMs for collaborative filtering
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Compressed Sensing and Dynamic Mode Decomposition
This video illustrates how to leverage compressed sensing to compute the dynamic mode decomposition (DMD) from under-sampled or compressed data. From the Paper: Compressed Sensing and Dynamic Mode Decomposition. JCD 2(2):165—191, 2015. Steven L. Brunton, Joshua L. Proctor, Jonathan H.
From playlist Research Abstracts from Brunton Lab
Sparse Sensor Placement Optimization for Reconstruction
This video discusses the important problem of how to select the fewest and most informative sensors to estimate a high-dimensional data set. I will discuss the algorithm and give several examples from control theory, to insect flight, to manufacturing. Book Website: http://databookuw.c
From playlist Sparsity and Compression [Data-Driven Science and Engineering]
Sparse Representation (for classification) with examples!
Follow updates on Twitter @eigensteve This video describes how to sparsely approximate data in an overcomplete library of examples. This algorithm has had profound impact in the past few decades for data analysis and machine learning. I will also include some examples in fluid dynamics
From playlist Sparsity and Compression [Data-Driven Science and Engineering]
35th Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series Talk
Title: Orthogonality sampling methods for solving electromagnetic inverse scattering problems Date: November 17, 2021, 10:00am Eastern Time Zone (US & Canada) / 2:00pm GMT Speaker: Dinh-Liem Nguyen, Kansas State University Abstract: Broadly speaking, inverse scattering problems are the pr
From playlist Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series
Reconstruction in Algebraic Geometry - Peter Haine
Spring Opportunities Workshop 2023 Topic: Reconstruction in Algebraic Geometry Speaker: Peter Haine Affiliation: IAS Date: January 12, 2023 A classical theorem of Neukirch and Uchida says that number fields are completely determined by their absolute Galois groups. One might wonder abou
From playlist Spring Opportunities Workshop 2023
Stefan Chmiela - Accurate global machine learning force fields for molecules with hundreds of atoms
Recorded 25 January 2023. Stefan Chmiela of the Technische Universität Berlin, Machine Learning, presents "Accurate global machine learning force fields for molecules with hundreds of atoms" at IPAM's Learning and Emergence in Molecular Systems Workshop. Abstract: Global machine learning f
From playlist 2023 Learning and Emergence in Molecular Systems
Worldwide Calculus: Vector Fields
Lecture on 'Vector Fields' from 'Worldwide Multivariable Calculus'. For more lecture videos and $10 digital textbooks, visit www.centerofmath.org.
From playlist Integration and Vector Fields