In this course you will examine real world problems -- rescue the Apollo 13 astronauts, stop the spread of epidemics, and fight forest fires -- involving differential equations and figure out how to solve them using numerical methods.
By the end of this course, you'll develop an intuition for the use of differential equations in the applied sciences. You'll also learn how to build mathematical models for systems of differential equations. Along the way, you'll learn how to translate mathematical expressions into Python code, and solve some really cool problems!
You'll need a basic knowledge of programming for this course, around the level of CS 101 or equivalent. You'll also need to understand trigonometry at the high school level, as well as basic vector algebra. This class will primarily involve solving equations numerically rather than analytically, but some exposure to calculus and physics at the level of PH 100 wouldn't hurt.
Introduction to the Forward Euler Method
Comparing solvers, Heun’s Method and Symplectic Euler Method
Implicit methods and stiffness
Stability, sensitivity, and optimization
Friction, equilibria, and control theory
Partial differential equations and heat conduction
Chaos, software, and predictive capability
This class is self paced. You can begin whenever you like and then follow your own pace. It’s a good idea to set goals for yourself to make sure you stick with the course.
This class will always be available!
Take a look at the “Class Summary,” “What Should I Know,” and “What Will I Learn” sections above. If you want to know more, just enroll in the course and start exploring.
Yes! The point is for you to learn what YOU need (or want) to learn. If you already know something, feel free to skip ahead. If you ever find that you’re confused, you can always go back and watch something that you skipped.
It’s completely free! If you’re feeling generous, we would love to have you contribute your thoughts, questions, and answers to the course discussion forum.
Collaboration is a great way to learn. You should do it! The key is to use collaboration as a way to enhance learning, not as a way of sharing answers without understanding them.
Udacity classes are a little different from traditional courses. We intersperse our video segments with interactive questions. There are many reasons for including these questions: to get you thinking, to check your understanding, for fun, etc... But really, they are there to help you learn. They are NOT there to evaluate your intelligence, so try not to let them stress you out.
Learn actively! You will retain more of what you learn if you take notes, draw diagrams, make notecards, and actively try to make sense of the material.
Jörn Loviscach is a professor of technical mathematics and computer engineering. In a former life, he was a professor of computer graphics, animation, and simulation, worked as editor and senior editor for several computer magazines. For three years he also served as deputy editor-in-chief of c’t Magazin für Computertechnik, a renowned German biweekly. His research integrates human-computer interaction, visual computing, and audio and music computing. Both as a researcher and as a practitioner he is interested in applying digital media to education. He has published over 1800 video lectures on his YouTube channel on mathematics and computer science, and his channel is one of the most popular of its kind in Germany.
Miriam feels inspired by the inherent beauty of math and loves helping others develop confidence and excitement as they learn. Having the opportunity to do this with thousands of students all over the world through her work at Udacity is a dream come true. Outside of work, she enjoys singing, baking, teaching yoga, and tutoring high schoolers. Miriam attended Stanford University, where she earned a B.S. in Physics, a B.A. in Philosophy and Religious Studies, and an M.A. in Religious Studies.