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Basic Concepts in Computational Physics
Nonlinear Dynamical Systems and Control. VijaySekhar Chellaboina. Classical Mechanics with Maxima. Todd Keene Timberlake. Multirate and Wavelet Signal Processing. Bruce W. We look at something in real life and try to make a model of it. We can use this model to predict future or new events in real life. If the model doesn't agree with real data, we change the model.
Repeat forever. OK, but now I need to define a model. That's not too hard. A model can really be anything used to represent real life but not real life. Here are some examples of scientific models:. So, a model can be many different things. It doesn't have to be a mathematical model—but that's often what we see in science. Hopefully these are all the things we already agree on.
One final note on the nature of science. The process of making a model would be theoretical physics. Comparing a model to real world stuff would be experimental physics. A scientist can do both theoretical and experimental physics—but for big projects like High Energy Physics humans tend to focus on either model building or model testing.
Now let's have a pretend meeting. We are going to meet a physics professor that specializes in creating computational models of different things it really could be for anything. This professor will be in a "lab" that consists of lots of computers. There will probably be some pretty powerful computer clusters in that lab.
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Now for a conversation with this computational physicist. Here are some key points that will be brought up. Just about every computational scientist says the same thing but not all of them. When you write a computer program, it does indeed give you some numbers in the end. Also, it is true that you don't always know what these values will look like until you actually run the program. This doesn't mean it's like a real experiment. In the end, the program was made by a human and not real life. Seller Inventory n. Book Description Springer, Condition: Brand New.
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