Fractional Deterministic and Stochastic Models and Their Calibration
A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "Numerical and Computational Methods".
Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 5771
Special Issue Editors
Interests: deep learning; kernel learning; fractional engineering modeling; parameter estimation of (fractional) PDEs; mathematical modeling of complex systems; computational mechanics; computational mathematics; data science
Interests: numerical methods for fractional differential equations; algorithms for solving stochastic differential equations driven by fractional Brownian motion; numerical methods for delay differential equations
Special Issue Information
Dear Colleagues,
Fractional calculus, when exploited and interpreted properly, gives us varying approaches to capturing and discovering memory effects, nonlocality, and even universality among physical quantities. Fractional deterministic and stochastic modeling enriches the fractional models that could better interpret real physical phenomena. For validating the proposed models, model calibration is of great importance and could bring chances for finding universal parameters, which integer-order models may not find.
The Special Issue embraces the contributions regarding fractional deterministic and stochastic models, numerical techniques or theoretical justification for their calibration, and insights and outlooks (review papers) on potentials of fractional models in interpreting and discovering nature rules. The aim of the Special Issue is to attract attention of mathematicians, scientists, and engineers outside the fractional community, by providing more physical justifications for fractional models.
Potential topics include, but are not limited to
- Fractional modeling in acoustic waves, hydrodynamics, viscoelasticity, fluid/solid mechanics, turbulence, finance, biology, physics, control systems, etc.;
- Numerical methods for Fractional differential equations with random inputs;
- Numerical methods for stochastic differential equation driven by fractional Brownian motion;
- Machine learning and other inversion techniques for fractional inverse problems;
- Wellpossedness analysis of fractional inverse problems.
Dr. Guofei Pang
Dr. Wanrong Cao
Guest Editors
Manuscript Submission Information
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Keywords
- Fractional calculus modeling
- Fractional differential equations with random inputs
- Fractional PDEs
- Fractional inverse problems
- Fractional Brownian motion
- Machine learning
- Numerical methods
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