Fractional Calculus and Advanced Applications in Complex and Nonlinear Systems
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Organizer(s): |
Name:
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Affiliation:
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Country:
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Yeliz Karaca
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University of Massachusetts Chan Medical School, Worcester, MA, USA
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USA
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Dumitru Baleanu
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Lebanese American University, Beirut, Lebanon and Institute of Space Science, Magurele, Ilfov, Romania
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Romania
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Muhammed Syam
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United Arab Emirates University
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United Arab Emirates
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Introduction:
| | Complex and nonlinear dynamic models are marked by intricate attributes including high dimensionality and heterogeneity, having fractional-order derivatives as well as comprising fractional calculus, which entails a profound comprehension and control of the multilayered dynamics and structure. By the investigation of fractional-order integral and derivative operators with real or complex domains emerging with progresses in feasible advanced computing technologies besides fractional calculus. Congruently, computational processing analyses can be facilitating in tackling complex and nonlinear dynamic problems through novel strategies based on observations and complex data.
Fractional models can handle phenomena manifesting memory effects in contrast with conventional models of ordinary and partial differential equations. Compared with integer-order calculus, fractional calculus can provide better affordances to tackle the observed time-dependent impacts besides generalized memory. Furthermore, Artificial Intelligence (AI), machine learning and deep learning methods lie in the production of new types of intelligent machines capable of responding correspondingly to human intelligence. Major advances in research and practice enable harnessing of volumes data, software utilized for algorithms and predictive models, computing with high-performance as well as the workforce involved, which all point towards the crosscutting nature of AI providing significant power for research formulization in a prioritized and customized way.
These positions can act as a bridge between mathematics and computer science along with other multiple range of sciences so that transition from integer to fractional order methods can be ensured. Advanced applications accordingly help reduce burden, workload and cost with various solutions differing depending on the fields and expertise. All these above-provided aspects are significant for optimal predictive solutions, critical decision-making processes, optimization, quantification, multiplicity, controllability, observability, synchronization and stabilization of fractional, neural, mathematical and computational systems among many other ones.
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List of approved abstract |
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