Evaluating Recurrence Relations (1 of 4: When do you apply Recurrence Relations?)
More resources available at www.misterwootube.com
From playlist Further Integration
Recurrence Relation Solution - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Sequences: Introduction to Solving Recurrence Relations
This video introduces solving recurrence relations by the methods of inspection, telescoping, and characteristic root technique. mathispower4u.com
From playlist Sequences (Discrete Math)
Will Rule 30 Help Me Find Gold?
The thesis is that geological mineralising systems may be considered as chemical reactors, incorporating interactions among deformation, heat, fluid flow and chemical reactions. These physical phenomena may be described by nonlinear dynamics, with possibly chaotic resulting behaviours. The
From playlist Wolfram Technology Conference 2020
Solve the Recurrence Relation by Backtracking: a_n = a_(n-1)
In this video I will show you how to solve a recurrence relation by using the method of backtracking. I hope this video helps someone.
From playlist Recurrence Relations
Recurrence Relations (3 of 4: Applying to Trigonometric Functions raised to power of n)
More resources available at www.misterwootube.com
From playlist Further Integration
How to Solve a Recurrence Relation using Backtracking: a_n = 2a_(n-1)
In this video I go through the steps of solving a recurrence relation using something called backtracking. This is a simple example so if you are new to this it may be useful. This is something you typically see in a discrete math class. I hope this video helps someone:)
From playlist Recurrence Relations
Petros Koumoutsakos - Alloys: Artificial Intelligence and Scientific Computing for Fluid Mechanics
Recorded 11 January 2023. Petros Koumoutsakos of Harvard University presents "Alloys: Artificial Intelligence and Scientific Computing for Fluid Mechanics" at IPAM's Explainable AI for the Sciences: Towards Novel Insights Workshop. Abstract: Over the last last thirty years we have experien
From playlist 2023 Explainable AI for the Sciences: Towards Novel Insights
Discrete Math - 2.4.2 Recurrence Relations
What is a recurrence relation, and how can we write it as a closed function? Textbook: Rosen, Discrete Mathematics and Its Applications, 7e Playlist: https://www.youtube.com/playlist?list=PLl-gb0E4MII28GykmtuBXNUNoej-vY5Rz
From playlist Discrete Math I (Entire Course)
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 | Uncertainty quantification and deep learning for water-hazard prediction by Ajay Harish
Description: As a typhoon makes landfall, it can result in high waves, high winds and a region of low pressure. The difference in the observed and regular sea level can be attributed to this advancing typhoon and is known as storm surge. Such surge when combined with the waves can lead to
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Leonid Kruglyak: "Genetic basis of phenotypic variation"
Computational Genomics Summer Institute 2017 "Genetic basis of phenotypic variation" Leonid Kruglyak, University of California, Los Angeles Institute for Pure and Applied Mathematics, UCLA July 13, 2017 For more information: http://computationalgenomics.bioinformatics.ucla.edu/
From playlist Computational Genomics Summer Institute 2017
DSI | MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validation
The utilization of large and complex data by machine learning in support of decision-making is of increasing importance in many scientific and national security domains. However, the need for uncertainty estimates or similar confidence indicators inhibits the integration of many popular ma
From playlist DSI Virtual Seminar Series
Deep Symbolic Regression: Recovering Math Expressions from Data via Risk-Seeking Policy Gradients
The Data Science Institute (DSI) hosted a virtual seminar by Brenden Petersen from Lawrence Livermore National Laboratory on April 22, 2021. Read more about the DSI seminar series at https://data-science.llnl.gov/latest/seminar-series. Discovering the underlying mathematical expressions d
From playlist DSI Virtual Seminar Series
DDPS | Parameter Subset Selection and Active Subspace Techniques for Engineering & Biological Models
Engineering and biological models generally have a number of parameters which are nonidentifiable in the sense that they are not uniquely determined by measured responses. Furthermore, the computational cost of high-fidelity simulation codes often precludes their direct use for Bayesian m
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Evaluating Recurrence Relations (4 of 4: Finding a term free of integrals)
More resources available at www.misterwootube.com
From playlist Further Integration
Some thoughts on Gaussian processes for emulation of deterministic computer models: Michael Stein
Uncertainty quantification (UQ) employs theoretical, numerical and computational tools to characterise uncertainty. It is increasingly becoming a relevant tool to gain a better understanding of physical systems and to make better decisions under uncertainty. Realistic physical systems are
From playlist Effective and efficient gaussian processes