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| 049 | _aMAIN | ||
| 100 | 1 |
_aCharbonneau, Paul, _d1961- _eauthor. _964903 |
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| 245 | 1 | 0 |
_aNatural complexity : _ba modeling handbook / _cPaul Charbonneau. |
| 264 | 1 |
_aPrinceton, NJ : _bPrinceton University Press, _c[2017] |
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| 300 |
_a1 online resource : _billustrations |
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_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 490 | 1 | _aPrimers in complex systems | |
| 504 | _aIncludes bibliographical references and index. | ||
| 588 | 0 | _aPrint version record. | |
| 505 | 0 | _aPart 1. What Is complexity?: Complexity Is not simple -- Randomness Is not complexity -- Chaos Is not complexity -- Open dissipative systems -- Natural complexity -- About the computer programs listed in this book -- Part 2. Iterated growth: Cellular automata in one spatial dimension -- Cellular automata in two spatial dimensions -- A zoo of 2-D structures from simple rules -- Agents, ants, and highways -- Emergent structures and behaviors -- Part 3. Aggregation: Diffusion-limited aggregation -- Numerical implementation -- A representative simulation -- A zoo of aggregates -- Fractal geometry -- Self-similarity and scale invariance -- Part 4. Percolation: Percolation in one dimension -- Percolation in two dimensions -- Cluster sizes -- Fractal clusters -- Is it really a power law? -- Criticality -- Part 5. Sandpiles: Model definition -- Numerical implementation -- A representative simulation -- Measuring avalanches -- Self-organized criticality -- Part 6. Forest fires: Model definition -- Numerical implementation -- A representative simulation -- Model behavior -- Back to criticality -- The pros and cons of wildfire management -- Part 7. Traffic jams: Model definition -- Numerical implementation -- A representative simulation -- Model behavior -- Traffic jams as avalanches -- Car traffic as a SOC system? -- Part 8. Earthquakes: The Burridge-Knopoff model -- Numerical implementation -- A representative simulation -- Model behavior -- Predicting real earthquakes -- Part 9. Epidemics: Model definition -- Numerical implementation -- A representative simulation -- Model behavior -- Epidemic self-organization -- Small-world networks -- Part 10. Flocking -- Model definition -- Numerical implementation -- A behavioral zoo -- Segregation of active and passive flockers -- Why you should never panic -- Part 11. Pattern formation: Excitable systems -- The hodgepodge machine -- Numerical implementation -- Waves, spirals, spaghettis, and cells -- Spiraling out -- Spontaneous pattern formation -- Part 12. Epilogue: A hike on slickrock -- Johannes Kepler and the unity of nature -- From lichens to solar flares -- Emergence and natural order -- Into the abyss: your turn -- Part A. Basic elements of the Python programming language: Code structure -- Variables and arrays -- Operators -- Loop constructs -- Conditional constructs -- Input/output and graphics -- Part B. Probability density functions: A simple example -- Continuous PDFs -- Some mathematical properties of power-law PDFs -- Cumulative PDFs -- PDFs with logarithmic bin sizes -- Better fits to power-law PDFs -- Part C. Random Numbers and walks: Random deviates -- The classical random walk -- Random walk and diffusion -- Part D. Lattice computation: Nearest-neighbor templates -- Periodic boundary conditions -- Random walks on lattices. | |
| 520 | _aThis book provides a short, hands-on introduction to the science of complexity using simple computational models of natural complex systems--with models and exercises drawn from physics, chemistry, geology, and biology. By working through the models and engaging in additional computational explorations suggested at the end of each chapter, readers very quickly develop an understanding of how complex structures and behaviors can emerge in natural phenomena as diverse as avalanches, forest fires, earthquakes, chemical reactions, animal flocks, and epidemic diseases. Natural Complexity provides the necessary topical background, complete source codes in Python, and detailed explanations for all computational models. Ideal for undergraduates, beginning graduate students, and researchers in the physical and natural sciences, this unique handbook requires no advanced mathematical knowledge or programming skills and is suitable for self-learners with a working knowledge of precalculus and high-school physics. Self-contained and accessible, Natural Complexity enables readers to identify and quantify common underlying structural and dynamical patterns shared by the various systems and phenomena it examines, so that they can form their own answers to the questions of what natural complexity is and how it arises. | ||
| 546 | _aIn English. | ||
| 590 |
_aIEEE _bIEEE Xplore Princeton University Press eBooks Library |
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| 650 | 0 |
_aComplexity (Philosophy) _964904 |
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| 650 | 0 |
_aPhysics _xMethodology. _964905 |
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| 650 | 0 |
_aComputational complexity. _93729 |
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| 650 | 0 |
_aBiocomplexity. _964906 |
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| 650 | 6 |
_aComplexit�e (Philosophie) _964907 |
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| 650 | 6 |
_aPhysique _xM�ethodologie. _964908 |
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| 650 | 6 |
_aComplexit�e de calcul (Informatique) _964909 |
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| 650 | 6 |
_aBiocomplexit�e. _964910 |
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| 650 | 7 |
_aSCIENCE _xPhilosophy & Social Aspects. _2bisacsh _922036 |
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| 650 | 7 |
_aCOMPUTERS _xData Modeling & Design. _2bisacsh _964145 |
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| 650 | 7 |
_aBiocomplexity. _2fast _0(OCoLC)fst01737618 _964906 |
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| 650 | 7 |
_aComplexity (Philosophy) _2fast _0(OCoLC)fst00871602 _964904 |
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| 650 | 7 |
_aComputational complexity. _2fast _0(OCoLC)fst00871991 _93729 |
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| 650 | 7 |
_aPhysics _xMethodology. _2fast _0(OCoLC)fst01063075 _964905 |
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_aElectronic books. _93294 |
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_aPrimers in complex systems. _964911 |
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