More Like this

1. Applications of variable-order fractional operators: a review

   https://doi.org/10.1098/rspa.2019.0498  

   Patnaik, Sansit; Hollkamp, John P.; Semperlotti, Fabio (February 2020, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences)

   Variable-order fractional operators were conceived and mathematically formalized only in recent years. The possibility of formulating evolutionary governing equations has led to the successful application of these operators to the modelling of complex real-world problems ranging from mechanics, to transport processes, to control theory, to biology. Variable-order fractional calculus (VO-FC) is a relatively less known branch of calculus that offers remarkable opportunities to simulate interdisciplinary processes. Recognizing this untapped potential, the scientific community has been intensively exploring applications of VO-FC to the modelling of engineering and physical systems. This review is intended to serve as a starting point for the reader interested in approaching this fascinating field. We provide a concise and comprehensive summary of the progress made in the development of VO-FC analytical and computational methods with application to the simulation of complex physical systems. More specifically, following a short introduction of the fundamental mathematical concepts, we present the topic of VO-FC from the point of view of practical applications in the context of scientific modelling. 

   more » « less
2. Identifying Arguments of Space-Time Fractional Diffusion: Data-Driven Approach

   https://doi.org/https://doi.org/10.3389/fams.2020.00014  

   Znaidi Mohamed Ridha, Gupta Gaurav (May 2020, Frontiers in applied mathematics and statistics)

   null (Ed.)

   A plethora of complex dynamical systems from disordered media to biological systems exhibit mathematical characteristics (e.g., long-range dependence, self-similar and power law magnitude increments) that are well-fitted by fractional partial differential equations (PDEs). For instance, some biological systems displaying an anomalous diffusion behavior, which is characterized by a non-linear mean-square displacement relation, can be mathematically described by fractional PDEs. In general, the PDEs represent various physical laws or rules governing complex dynamical systems. Since prior knowledge about the mathematical equations describing complex dynamical systems in biology, healthcare, disaster mitigation, transportation, or environmental sciences may not be available, we aim to provide algorithmic strategies to discover the integer or fractional PDEs and their parameters from system's evolution data. Toward deciphering non-trivial mechanisms driving a complex system, we propose a data-driven approach that estimates the parameters of a fractional PDE model. We study the space-time fractional diffusion model that describes a complex stochastic process, where the magnitude and the time increments are stable processes. Starting from limited time-series data recorded while the system is evolving, we develop a fractional-order moments-based approach to determine the parameters of a generalized fractional PDE. We formulate two optimization problems to allow us to estimate the arguments of the fractional PDE. Employing extensive simulation studies, we show that the proposed approach is effective at retrieving the relevant parameters of the space-time fractional PDE. The presented mathematical approach can be further enhanced and generalized to include additional operators that may help to identify the dominant rule governing the measurements or to determine the degree to which multiple physical laws contribute to the observed dynamics. 

   more » « less
3. A data-driven framework for discovering fractional differential equations in complex systems

   https://doi.org/10.1007/s11071-025-11373-z  

   Xiangnan, Yu; Hao, Xu; Zhiping, Mao; Hongguang, Sun; Yong, Zhang; Zhibo, Chen; Dongxiao, Zhang; Yuntian, Chen (May 2025, Nonlinear Dynamics)

   In complex physical systems, conventional differential equations fall short in capturing non-local and memory effects. Fractional differential equations (FDEs) effectively model long-range interactions with fewer parameters. However, deriving FDEs from physical principles remains a significant challenge. This study introduces a stepwise data-driven framework to discover explicit expressions of FDEs directly from data. The proposed framework combines deep neural networks for data reconstruction and automatic differentiation with Gauss-Jacobi quadrature for fractional derivative approximation, effectively handling singularities while achieving fast, high-precision computations across large temporal/spatial scales. To optimize both linear coefficients and the nonlinear fractional orders, we employ an alternating optimization approach that combines sparse regression with global optimization techniques. We validate the framework on various datasets, including synthetic anomalous diffusion data, experimental data on the creep behavior of frozen soils, and single-particle trajectories modeled by Lévy motion. Results demonstrate the framework’s robustness in identifying FDE structures across diverse noise levels and its ability to capture integer order dynamics, offering a flexible approach for modeling memory effects in complex systems. 

   more » « less
4. Closed Form Time Response of an Infinite Tree of Mechanical Components Described by an Irrational Transfer Function

   Guel-Cortez, A. J.; Sen, M.; Goodwine, B. (July 2019, Proceedings of the ... American Control Conference)

   In this work we determine the time-domain dynamics of a complex mechanical network of integer-order components, e.g., springs and dampers, with an overall transfer function described by implicitly defined operators. This type of transfer functions can be used to describe very large scale dynamics of robot formations, multi-agent systems or viscoelastic phenomena. Such large-scale integrated systems are becoming increasingly important in modern engineering systems, and an accurate model of their dynamics is very important to achieve their control. We give a time domain representation for the dynamics of the system by using a complex variable analysis to find its impulse response. Furthermore, we validate how our infinite order model can be used to describe dynamics of finite order networks, which can be useful as a model reduction method. 

   more » « less
5. Physics-Informed Neural Network-Based Inverse Framework for Time-Fractional Differential Equations for Rheology

   https://doi.org/10.3390/biology14070779  

   Thakur, Sukirt; Mitra, Harsa; Ardekani, Arezoo M (July 2025, Biology)

   Inverse problems involving time-fractional differential equations have become increasingly important for modeling systems with memory-dependent dynamics, particularly in biotransport and viscoelastic materials. Despite their potential, these problems remain challenging due to issues of stability, non-uniqueness, and limited data availability. Recent advancements in Physics-Informed Neural Networks (PINNs) offer a data-efficient framework for solving such inverse problems, yet most implementations are restricted to integer-order derivatives. In this work, we develop a PINN-based framework tailored for inverse problems involving time-fractional derivatives. We consider two representative applications: anomalous diffusion and fractional viscoelasticity. Using both synthetic and experimental datasets, we infer key physical parameters including the generalized diffusion coefficient and the fractional derivative order in the diffusion model and the relaxation parameters in a fractional Maxwell model. Our approach incorporates a customized residual loss function scaled by the standard deviation of observed data to enhance robustness. Even under 25% Gaussian noise, our method recovers model parameters with relative errors below 10%. Additionally, the framework accurately predicts relaxation moduli in porcine tissue experiments, achieving similar error margins. These results demonstrate the framework’s effectiveness in learning fractional dynamics from noisy and sparse data, paving the way for broader applications in complex biological and mechanical systems. 

   more » « less