6.336J is an introduction to computational techniques for the simulation of a large variety of engineering and physical systems. Applications are drawn from aerospace, mechanical, electrical, chemical and biological engineering, and materials science. Topics include: mathematical formulations; network problems; sparse direct and iterative matrix solution techniques; Newton methods for nonlinear problems; discretization methods for ordinary, time-periodic and partial differential equations, fast methods for partial differential and integral equations, techniques for dynamical system model reduction and approaches for molecular dynamics.
This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5211 (Introduction to Numerical Simulation).

Introduction to the Modeling and Analysis of Complex Systems introduces students to mathematical/computational modeling and analysis developed in the emerging interdisciplinary field of Complex Systems Science. Complex systems are systems made of a large number of microscopic components interacting with each other in nontrivial ways. Many real-world systems can be understood as complex systems, where critically important information resides in the relationships between the parts and not necessarily within the parts themselves. This textbook offers an accessible yet technically-oriented introduction to the modeling and analysis of complex systems. The topics covered include: fundamentals of modeling, basics of dynamical systems, discrete-time models, continuous-time models, bifurcations, chaos, cellular automata, continuous field models, static networks, dynamic networks, and agent-based models. Most of these topics are discussed in two chapters, one focusing on computational modeling and the other on mathematical analysis. This unique approach provides a comprehensive view of related concepts and techniques, and allows readers and instructors to flexibly choose relevant materials based on their objectives and needs. Python sample codes are provided for each modeling example.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"A new control design could help engineers improve the stability and optimality of long, slender beams, including those used for offshore engineering. Numerous important dynamical systems are governed by nonlinear partial differential equations: from chemical reactions to epidemics to engineering structures. While optimal control designs have been attempted for these highly complex systems, doing so is extremely difficult. The inverse control approach has proven useful for extending optimal designs from linear to nonlinear systems but, for the most part, only for Euclidean spaces. Extension to Hilbert spaces faces difficulties due to infinite-dimension and the formidable obstacle of having to solve a Hamilton–Jacobi–Bellman equation. Now, researchers have found a way to surmount that barrier, formulating a control design that can be used to reliably stabilize extensible and shearable beams..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

Learn Differential Equations: Up Close with _Gilbert Strang and_ Cleve Moler is an in-depth series of videos about differential equations and the MATLAB® ODE suite. These videos are suitable for students and life-long learners to enjoy.
About the Instructors
Gilbert Strang is the MathWorks Professor of Mathematics at MIT. His research focuses on mathematical analysis, linear algebra and PDEs. He has written textbooks on linear algebra, computational science, finite elements, wavelets, GPS, and calculus.
Cleve Moler is chief mathematician, chairman, and cofounder of MathWorks. He was a professor of math and computer science for almost 20 years at the University of Michigan, Stanford University, and the University of New Mexico.
These videos were produced by The MathWorks and are also available on The MathWorks website.

MTH 256 at Portland Community College includes a variety of differential equations and their solutions, with emphasis on applied problems in engineering and physics. Uses differential equations software. Students communicate results in oral and written form. Graphing and Computer Algebra System (CAS) technology are used, such as Desmos and/or GeoGebra which are available at no cost. This is a one-term introduction to ordinary differential equations with applications. Topics include classification of, and what is meant by the solution of a differential equation, first-order equations for which exact solutions are obtainable, explicit methods of solving higher-order linear differential equations, an introduction to systems of differential equations, and the Laplace transform. Applications of first-order linear differential equations and second-order linear differential equations with constant coefficients will be studied.

With this supplement, we are adding to the work of Thomas Judson's “The Ordinary Differential Equations Project” by providing additional practice problems that mostly focus on applications. We worked with the 2022 edition 2  of The Ordinary Differential Equations Project 3 .

In sections 2.2, 2.4, 3.2, 3.3, and 3.4, we utilize the work and ideas of Steven Strogatz in his paper titled “Love Affairs and Differential Equations” 4 , Published in the February 1988 edition of Mathematics Magazine. This paper and several others expanding these ideas can be found by googling “Romeo and Juliet differential equations”.

Mathematics explained: Here you find videos on various math topics:

Pre-university Calculus (functions, equations, differentiation and integration)
Vector calculus (preparation for mechanics and dynamics courses)
Differential equations, Calculus
Functions of several variables, Calculus
Linear Algebra
Probability and Statistics

How do populations grow? How do viruses spread? What is the trajectory of a glider?

Many real-life problems can be described and solved by mathematical models. In this course, you will form a team with another student and work in a project to solve a real-life problem.

You will learn to analyze your chosen problem, formulate it as a mathematical model (containing ordinary differential equations), solve the equations in the model, and validate your results. You will learn how to implement Euler’s method in a Python program.

If needed, you can refine or improve your model, based on your first results. Finally, you will learn how to report your findings in a scientific way.

This course is mainly aimed at Bachelor students from Mathematics, Engineering and Science disciplines. However it will suit anyone who would like to learn how mathematical modeling can solve real-world problems.

This course covers the mathematical techniques necessary for understanding of materials science and engineering topics such as energetics, materials structure and symmetry, materials response to applied fields, mechanics and physics of solids and soft materials. The class uses examples from the materials science and engineering core courses (3.012 and 3.014) to introduce mathematical concepts and materials-related problem solving skills. Topics include linear algebra and orthonormal basis, eigenvalues and eigenvectors, quadratic forms, tensor operations, symmetry operations, calculus of several variables, introduction to complex analysis, ordinary and partial differential equations, theory of distributions, and fourier analysis.

This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis.

This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming.

This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming.

This course is an introduction to numerical methods: interpolation, differentiation, integration, and systems of linear equations. It covers the solution of differential equations by numerical integration, as well as partial differential equations of inviscid hydrodynamics: finite difference methods, boundary integral equation panel methods. Also addressed are introductory numerical lifting surface computations, fast Fourier transforms, the numerical representation of deterministic and random sea waves, as well as integral boundary layer equations and numerical solutions.
This course was originally offered in Course 13 (Department of Ocean Engineering) as 13.024. In 2005, ocean engineering subjects became part of Course 2 (Department of Mechanical Engineering), and this course was renumbered 2.29.

In this book we discuss several numerical methods for solving ordinary differential equations. We emphasize the aspects that play an important role in practical problems. We confine ourselves to ordinary differential equations with the exception of the last chapter in which we discuss the heat equation, a parabolic partial differential equation. The techniques discussed in the intro-ductory chapters, for instance interpolation, numerical quadrature and the solution to nonlinear equations, may also be used outside the context of differential equations. They have been in-cluded to make the book self-contained as far as the numerical aspects are concerned. Chapters, sections and exercises marked with a * are not part of the Delft Institutional Package.
The numerical examples in this book were implemented in Matlab, but also Python or any other programming language could be used. A list of references to background knowledge and related literature can be found at the end of this book. Extra information about this course can be found at http://NMODE.ewi.tudelft.nl, among which old exams, answers to the exercises, and a link to an online education platform. We thank Matthias Moller for his thorough reading of the draft of this book and his helpful suggestions.

A presentation of the fundamentals of modern numerical techniques for a wide range of linear and nonlinear elliptic, parabolic and hyperbolic partial differential equations and integral equations central to a wide variety of applications in science, engineering, and other fields. Topics include: Mathematical Formulations; Finite Difference and Finite Volume Discretizations; Finite Element Discretizations; Boundary Element Discretizations; Direct and Iterative Solution Methods.
This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5212 (Numerical Methods for Partial Differential Equations).

Open Source Calculus and Analysis - A comprehensive textbook introducing the main concepts from calculus and analysis

Features
- Comprehensiveness: all from calculus and analysis that is needed in first year mathematics-oriented programmes
- Mathematical rigour: formal definitions, extended theorems and proofs
- Includes differential equations
- Applicability: integrated symbolic (Mathematica or SymPy) and numerical computation (Python), review exercises, and so on
- Accessibility: final solution to all exercises, QR codes to accompanying videos,…
- Flexibility: using dedicated if-loops in the underlying Latex-code you can compile the version that suits your needs (Mathematica or Sympy, Calculus only or analysis as well, with or without differential equations,…)

The Ordinary Differential Equation Project is an open source textbook designed to teach ordinary differential equations to undergraduates. This is a work in progress by Thomas W. Judson. The books strengths will include a wide range of exercises, both computational and theoretical, plus many nontrivial applications. Technology is integrated into the textbook using the open source system, Sage.

This course introduces dynamic processes and the engineering tasks of process operations and control. Subject covers modeling the static and dynamic behavior of processes; control strategies; design of feedback, feedforward, and other control structures; and applications to process equipment.
Dedication
In preparing this material, the author has recalled with pleasure his own introduction, many years ago, to Process Control. This OCW course is dedicated with gratitude, to Prof. W. C. Clements of the University of Alabama.

Differential Equations and Linear Algebra is a free and open textbook introducing the basics of differential equations and linear algebra to undergraduate students. Students should have taken courses in Differential and Integral Calculus before using this textbook. This book is a combination of 3 open educational resources: 1. Elementary Differential Equations by William F. Trench, licensed under CC BY-NC-SA 3.0. 2. Differential Equations for Engineers by Jiří Lebl, licensed under CC BY-NC-SA 3.0. 3. A First Course in Linear Algebra (an open text) by Lyryx Learning – based on original text by Ken Kuttler, licensed under CC BY.

This unit includes 5 lessons that culminates in a persuasive argument in the form of letter to congressional member or grant proposal to Duke Energy.