Brian, a graduate student of Applied Mathematics at Columbia University, has a Tumblr called Fouriest Series where he posts his math and physics visualizations. His explanations are clearly written. He also provides the Mathematica code he used to create his animations. From his post about chaos and double pendulums:

Summarized by mathematician Edward Lorenz, "Chaos [is] when the present determines the future, but the approximate present does not approximately determine the future.“ There’s an important distinction to make between a chaotic system and a random system. Given the starting conditions, a chaotic system is entirely deterministic. A random system, on the other hand, is entirely non-deterministic, even when the starting conditions are known. That is, with enough information, the evolution of a chaotic system is entirely predictable, but in a random system there’s no amount of information that would be enough to predict the system’s evolution. The simulations above show two slightly different initial conditions for a double pendulum — an example of a chaotic system. In the left animation both pendulums begin horizontally, and in the right animation the red pendulum begins horizontally and the blue is rotated by 0.1 radians (≈ 5.73°) above the positive x-axis. In both simulations, all of the pendulums begin from rest.

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Brian, a graduate student of Applied Mathematics at Columbia University, has a Tumblr called Fouriest Series where he posts his math and physics visualizations. His explanations are clearly written. He also provides the Mathematica code he used to create his animations. From his post about chaos and double pendulums:

Summarized by mathematician Edward Lorenz, "Chaos [is] when the present determines the future, but the approximate present does not approximately determine the future.“ There’s an important distinction to make between a chaotic system and a random system. Given the starting conditions, a chaotic system is entirely deterministic. A random system, on the other hand, is entirely non-deterministic, even when the starting conditions are known. That is, with enough information, the evolution of a chaotic system is entirely predictable, but in a random system there’s no amount of information that would be enough to predict the system’s evolution. The simulations above show two slightly different initial conditions for a double pendulum — an example of a chaotic system. In the left animation both pendulums begin horizontally, and in the right animation the red pendulum begins horizontally and the blue is rotated by 0.1 radians (≈ 5.73°) above the positive x-axis. In both simulations, all of the pendulums begin from rest.

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Are you curious about complexity? Do you dig dynamic systems and emergent phenomena? The Santa Fe Institute, one of the birthplaces of chaos theory, is now offering a free "Introduction to Complexity" online course, open to anyone. No science or math background required! The instructor is computer scientist Melanie Mitchell, author of the excellent and entertaining book Complexity: A Guided Tour. The course started last week but it's not too late to join!

In this eleven-week course you'll learn about the tools used by scientists to understand complex systems. The topics you'll learn about include dynamics, chaos, fractals, information theory, self-organization, agent-based modeling, and networks. You’ll also get a sense of how these topics fit together to help explain how complexity arises and evolves in nature, society, and technology. There are no prerequisites. You don't need a science or math background to take this introductory course; it simply requires an interest in the field and the willingness to participate in a hands-on approach to the subject.Introduction to Complexity]]>

Are you curious about complexity? Do you dig dynamic systems and emergent phenomena? The Santa Fe Institute, one of the birthplaces of chaos theory, is now offering a free "Introduction to Complexity" online course, open to anyone. No science or math background required! The instructor is computer scientist Melanie Mitchell, author of the excellent and entertaining book Complexity: A Guided Tour. The course started last week but it's not too late to join!

In this eleven-week course you'll learn about the tools used by scientists to understand complex systems. The topics you'll learn about include dynamics, chaos, fractals, information theory, self-organization, agent-based modeling, and networks. You’ll also get a sense of how these topics fit together to help explain how complexity arises and evolves in nature, society, and technology. There are no prerequisites. You don't need a science or math background to take this introductory course; it simply requires an interest in the field and the willingness to participate in a hands-on approach to the subject.Introduction to Complexity]]>