Fractional Calculus and the Future of Science

Newton foresaw the limitations of geometry's description of planetary behavior and developed fluxions (differentials) as the new language for celestial mechanics and as the way to implement his laws of mechanics. Two hundred years later Mandelbrot introduced the notion of fractals into the sci...

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Other Authors: West, Bruce J. (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
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520 |a Newton foresaw the limitations of geometry's description of planetary behavior and developed fluxions (differentials) as the new language for celestial mechanics and as the way to implement his laws of mechanics. Two hundred years later Mandelbrot introduced the notion of fractals into the scientific lexicon of geometry, dynamics, and statistics and in so doing suggested ways to see beyond the limitations of Newton's laws. Mandelbrot's mathematical essays suggest how fractals may lead to the understanding of turbulence, viscoelasticity, and ultimately to end of dominance of the Newton's macroscopic world view.Fractional Calculus and the Future of Science examines the nexus of these two game-changing contributions to our scientific understanding of the world. It addresses how non-integer differential equations replace Newton's laws to describe the many guises of complexity, most of which lay beyond Newton's experience, and many had even eluded Mandelbrot's powerful intuition. The book's authors look behind the mathematics and examine what must be true about a phenomenon's behavior to justify the replacement of an integer-order with a noninteger-order (fractional) derivative. This window into the future of specific science disciplines using the fractional calculus lens suggests how what is seen entails a difference in scientific thinking and understanding. 
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650 7 |a Research & information: general  |2 bicssc 
650 7 |a Mathematics & science  |2 bicssc 
653 |a fractional diffusion 
653 |a continuous time random walks 
653 |a reaction-diffusion equations 
653 |a reaction kinetics 
653 |a multidimensional scaling 
653 |a fractals 
653 |a fractional calculus 
653 |a financial indices 
653 |a entropy 
653 |a Dow Jones 
653 |a complex systems 
653 |a Skellam process 
653 |a subordination 
653 |a Lévy measure 
653 |a Poisson process of order k 
653 |a running average 
653 |a complexity 
653 |a chaos 
653 |a logistic differential equation 
653 |a liouville-caputo fractional derivative 
653 |a local discontinuous Galerkin methods 
653 |a stability estimate 
653 |a Mittag-Leffler functions 
653 |a Wright functions 
653 |a fractional relaxation 
653 |a diffusion-wave equation 
653 |a Laplace and Fourier transform 
653 |a fractional Poisson process complex systems 
653 |a distributed-order operators 
653 |a viscoelasticity 
653 |a transport processes 
653 |a control theory 
653 |a fractional order PID control 
653 |a PMSM 
653 |a frequency-domain control design 
653 |a optimal tuning 
653 |a Gaussian watermarks 
653 |a statistical assessment 
653 |a false positive rate 
653 |a semi-fragile watermarking system 
653 |a fractional dynamics 
653 |a fractional-order thinking 
653 |a heavytailedness 
653 |a big data 
653 |a machine learning 
653 |a variability 
653 |a diversity 
653 |a telegrapher's equations 
653 |a fractional telegrapher's equation 
653 |a continuous time random walk 
653 |a transport problems 
653 |a fractional conservations laws 
653 |a variable fractional model 
653 |a turbulent flows 
653 |a fractional PINN 
653 |a physics-informed learning 
653 |a n/a 
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