Introduction to Hyperbolic Functions
This video provides a basic overview of hyperbolic function. The lesson defines the hyperbolic functions, shows the graphs of the hyperbolic functions, and gives the properties of hyperbolic functions.
From playlist Using the Properties of Hyperbolic Functions
Introduction to Hyperbolic Functions
This video provides a basic overview of hyperbolic function. The lesson defines the hyperbolic functions, shows the graphs of the hyperbolic functions, and gives the properties of hyperbolic functions. Site: http://mathispower4u.com Blog: http://mathispower4u.wordpress.com
From playlist Differentiation of Hyperbolic Functions
DDPS | Model order reduction assisted by deep neural networks (ROM-net)
In this talk from June 10, 2021, David Ryckelynck of MINES ParisTech University discusses a general framework for projection-based model order reduction assisted by deep neural networks. The proposed methodology, called ROM-net [1], consists in using deep learning techniques to adapt the
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Ling Long - Hypergeometric Functions, Character Sums and Applications - Lecture 5
Title: Hypergeometric Functions, Character Sums and Applications Speaker: Prof. Ling Long, Louisiana State University Abstract: Hypergeometric functions form a class of special functions satisfying a lot of symmetries. They are closely related to the arithmetic of one-parameter families of
From playlist Hypergeometric Functions, Character Sums and Applications
Ling Long - Hypergeometric Functions, Character Sums and Applications - Lecture 4
Title: Hypergeometric Functions, Character Sums and Applications Speaker: Prof. Ling Long, Louisiana State University Abstract: Hypergeometric functions form a class of special functions satisfying a lot of symmetries. They are closely related to the arithmetic of one-parameter families of
From playlist Hypergeometric Functions, Character Sums and Applications
Ling Long - Hypergeometric Functions, Character Sums and Applications - Lecture 2
Title: Hypergeometric Functions, Character Sums and Applications Speaker: Prof. Ling Long, Louisiana State University Abstract: Hypergeometric functions form a class of special functions satisfying a lot of symmetries. They are closely related to the arithmetic of one-parameter families of
From playlist Hypergeometric Functions, Character Sums and Applications
Lek-Heng Lim: "What is a tensor? (Part 2/2)"
Watch part 1/2 here: https://youtu.be/MkYEh0UJKcE Tensor Methods and Emerging Applications to the Physical and Data Sciences Tutorials 2021 "What is a tensor? (Part 2/2)" Lek-Heng Lim - University of Chicago, Statistics Abstract: We discuss the three best-known definitions of a tensor:
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Deniz Eroglu - Emergent hypernetworks in weakly coupled oscillators - IPAM at UCLA
Recorded 01 September 2022. Deniz Eroglu of Kadir Has University presents "Emergent hypernetworks in weakly coupled oscillators" at IPAM's Reconstructing Network Dynamics from Data: Applications to Neuroscience and Beyond. Abstract: Studies on model reconstruction from data have shown prev
From playlist 2022 Reconstructing Network Dynamics from Data: Applications to Neuroscience and Beyond
Ling Long - Hypergeometric Functions, Character Sums and Applications - Lecture 6
Title: Hypergeometric Functions, Character Sums and Applications Speaker: Prof. Ling Long, Louisiana State University Abstract: Hypergeometric functions form a class of special functions satisfying a lot of symmetries. They are closely related to the arithmetic of one-parameter families of
From playlist Hypergeometric Functions, Character Sums and Applications
Ling Long - Hypergeometric Functions, Character Sums and Applications - Lecture 3
Title: Hypergeometric Functions, Character Sums and Applications Speaker: Prof. Ling Long, Louisiana State University Abstract: Hypergeometric functions form a class of special functions satisfying a lot of symmetries. They are closely related to the arithmetic of one-parameter families of
From playlist Hypergeometric Functions, Character Sums and Applications
Ling Long - Hypergeometric Functions, Character Sums and Applications - Lecture 1
Title: Hypergeometric Functions, Character Sums and Applications Speaker: Prof. Ling Long, Louisiana State University Abstract: Hypergeometric functions form a class of special functions satisfying a lot of symmetries. They are closely related to the arithmetic of one-parameter families of
From playlist Hypergeometric Functions, Character Sums and Applications
DDPS | libROM: Library for physics-constrained data-driven physical simulations | Youngsoo Choi
A data-driven model can be built to accurately accelerate computationally expensive physical simulations, which is essential in multi-query problems, such as inverse problem, uncertainty quantification, design optimization, and optimal control. In this talk, two types of data-driven mode
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Frank Noé: "Intro to Machine Learning (Part 1/2)"
Watch part 2/2 here: https://youtu.be/7TZnGQrNF6g Machine Learning for Physics and the Physics of Learning Tutorials 2019 "Intro to Machine Learning (Part 1/2)" Frank Noé, Freie Universität Berlin Institute for Pure and Applied Mathematics, UCLA September 5, 2019 For more information:
From playlist Machine Learning for Physics and the Physics of Learning 2019
Ling Long - Hypergeometric Functions, Character Sums and Applications - Lecture 7
Title: Hypergeometric Functions, Character Sums and Applications Speaker: Prof. Ling Long, Louisiana State University Abstract: Hypergeometric functions form a class of special functions satisfying a lot of symmetries. They are closely related to the arithmetic of one-parameter families of
From playlist Hypergeometric Functions, Character Sums and Applications
Frank Noé: "Fundamentals of Artificial Intelligence and Machine Learning" (Part 1/2)
Watch part 2/2 here: https://youtu.be/gSLQ_2uFSiA Mathematical Challenges and Opportunities for Autonomous Vehicles Tutorials 2020 "Fundamentals of Artificial Intelligence and Machine Learning" (Part 1/2) Frank Noé - Freie Universität Berlin Institute for Pure and Applied Mathematics, U
From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020
DDPS | Neural architecture search for surrogate modeling
In this talk from May 27th, 2021, Romit Maulik of Argonne National Laboratory discusses recent results from the use of parallelized neural architecture search (NAS) for discovering non-intrusive surrogate models from data. NAS is deployed using DeepHyper, a scalable neural architecture and
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
DDPS | Deep learning for reduced order modeling
Description: Reduced order modeling (ROM) techniques, such as the reduced basis method, provide nowadays an essential toolbox for the efficient approximation of parametrized differential problems, whenever they must be solved either in real-time, or in several different scenarios. These ta
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Heather Harrington (11/2/2022): Shape of data in biology: Extending the PH pipeline
Spatial structure in scientific data is a hallmark of real-world complex systems. Topological data analysis provides a powerful computational window on the connectivity and shape of such systems across multiple scales. We first demonstrate how cycle representatives in persistent homology c
From playlist AATRN 2022
Ling Long - Hypergeometric Functions, Character Sums and Applications - Lecture 8
Title: Hypergeometric Functions, Character Sums and Applications Speaker: Prof. Ling Long, Louisiana State University Abstract: Hypergeometric functions form a class of special functions satisfying a lot of symmetries. They are closely related to the arithmetic of one-parameter families of
From playlist Hypergeometric Functions, Character Sums and Applications
Scalable hyperparameter transfer learning - Perrone - Workshop 3 - CEB T1 2019
Valerio Perrone (Amazon) / 01.04.2019 Scalable hyperparameter transfer learning. Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization, such as hyperparameter optimization. Typically, BO relies on conventional Gaussian process (GP) regres
From playlist 2019 - T1 - The Mathematics of Imaging