Differential Equations with Forcing: Method of Variation of Parameters
This video solves externally forced linear differential equations with the method of variation of parameters. This approach is extremely powerful. The idea is to solve the unforced, or "homogeneous" system, and then to replace the unknown coefficients c_k with unknown functions of time c
From playlist Engineering Math: Differential Equations and Dynamical Systems
Comparing Iterative and Recursive Factorial Functions
Comparing iterative and recursive factorial functions
From playlist Computer Science
From playlist Courses and Series
Differential Equations with Forcing: Method of Undetermined Coefficients
This video introduces external forcing to linear differential equations, and we show how to solve these equations with the method of undetermined coefficients. The idea is simple: 1) solve the unforced, or "homogeneous" system; 2) find a particular solution that equals the forcing when pl
From playlist Engineering Math: Differential Equations and Dynamical Systems
What is an Injective Function? Definition and Explanation
An explanation to help understand what it means for a function to be injective, also known as one-to-one. The definition of an injection leads us to some important properties of injective functions! Subscribe to see more new math videos! Music: OcularNebula - The Lopez
From playlist Functions
Differential Equations: Force Damped Oscillations
How to solve an application of non-homogeneous systems, forced damped oscillations. Special resonance review at the end.
From playlist Basics: Differential Equations
A15 More about undetermined coefficients
A more complex example of a linear set of differential equations solved by the technique of variation of parameters.
From playlist A Second Course in Differential Equations
How to Determine if Functions are Linearly Independent or Dependent using the Definition
How to Determine if Functions are Linearly Independent or Dependent using the Definition If you enjoyed this video please consider liking, sharing, and subscribing. You can also help support my channel by becoming a member https://www.youtube.com/channel/UCr7lmzIk63PZnBw3bezl-Mg/join Th
From playlist Zill DE 4.1 Preliminary Theory - Linear Equations
Ralf Schindler Universität Münster, Germany
From playlist Talks of Mathematics Münster's reseachers
Lec 10 | MIT Finite Element Procedures for Solids and Structures, Nonlinear Analysis
Lecture 10: Solution of Nonlinear Static FE Equations I Instructor: Klaus-Jürgen Bathe View the complete course: http://ocw.mit.edu/RES2-002S10 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT Nonlinear Finite Element Analysis
Deep Learning Lecture 4.3 - Stochastic Gradient Descent
Deep Learning Lecture: Optimization Methods - Stochastic Gradient Descent (SGD) - SGD with Momentum
From playlist Deep Learning Lecture
Physical insights from a numerical simulation of the dissipative Euler flow by Takeshi Matsumoto
PROGRAM TURBULENCE: PROBLEMS AT THE INTERFACE OF MATHEMATICS AND PHYSICS ORGANIZERS Uriel Frisch (Observatoire de la Côte d'Azur and CNRS, France), Konstantin Khanin (University of Toronto, Canada) and Rahul Pandit (IISc, India) DATE & TIME 16 January 2023 to 27 January 2023 VENUE Ramanuj
From playlist Turbulence: Problems at the Interface of Mathematics and Physics 2023
Lec 5 | MIT Finite Element Procedures for Solids and Structures, Nonlinear Analysis
Lecture 5: Updated Lagrangian formulation - incremental analysis Instructor: Klaus-Jürgen Bathe View the complete course: http://ocw.mit.edu/RES2-002S10 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT Nonlinear Finite Element Analysis
Lec 6 | MIT Finite Element Procedures for Solids and Structures, Nonlinear Analysis
Lecture 6: Formulation of finite element matrices Instructor: Klaus-Jürgen Bathe View the complete course: http://ocw.mit.edu/RES2-002S10 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT Nonlinear Finite Element Analysis
Lec 4 | MIT Finite Element Procedures for Solids and Structures, Nonlinear Analysis
Lecture 4: Total Lagrangian formulation - incremental analysis Instructor: Klaus-Jürgen Bathe View the complete course: http://ocw.mit.edu/RES2-002S10 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT Nonlinear Finite Element Analysis
Joel David Hamkins : The hierarchy of second-order set theories between GBC and KM and beyond
Abstract: Recent work has clarified how various natural second-order set-theoretic principles, such as those concerned with class forcing or with proper class games, fit into a new robust hierarchy of second-order set theories between Gödel-Bernays GBC set theory and Kelley-Morse KM set th
From playlist Logic and Foundations
Model Based Reinforcement Learning: Policy Iteration, Value Iteration, and Dynamic Programming
Here we introduce dynamic programming, which is a cornerstone of model-based reinforcement learning. We demonstrate dynamic programming for policy iteration and value iteration, leading to the quality function and Q-learning. This is a lecture in a series on reinforcement learning, follo
From playlist Reinforcement Learning
Linearising nonlinear derivatives
A simple trick to linearise derivatives
From playlist Linearisation
11. Unconstrained Optimization; Newton-Raphson and Trust Region Methods
MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015 View the complete course: http://ocw.mit.edu/10-34F15 Instructor: James Swan Students learned how to solve unconstrained optimization problems. In addition of the Newton-Raphson method, students also learned the steepe
From playlist MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015