Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you'll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics. Whether you're an intermediate-level Python programmer or a student of computational modeling, you'll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations. Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform; Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines; Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata; Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism. Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.
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| Pages : | 200 |
| Size : | |
| Downloads: | 133 |
| Created: | 2021-05-15 |
| License: | Free |