Numerical methods for nonlocal and fractional models
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Abstract (arXiv)
Partial differential equations (PDEs) are used, with huge success, to model phenomena arising across all scientific and engineering disciplines. However, across an equally wide swath, there exist situations in which PDE models fail to adequately model observed phenomena or are not the best available model for that purpose. On the other hand, in many situations, nonlocal models that account for interaction occurring at a distance have been shown to more faithfully and effectively model observed phenomena that involve possible singularities and other anomalies. In this article, we consider a generic nonlocal model, beginning with a short review of its definition, the properties of its solution, its mathematical analysis, and specific concrete examples. We then provide extensive discussions about numerical methods, including finite element, finite difference, and spectral methods, for determining approximate solutions of the nonlocal models considered. In that discussion, we pay particular attention to a special class of nonlocal models that are the most widely studied in the literature, namely those involving fractional derivatives. The article ends with brief considerations of several modeling and algorithmic extensions which serve to show the wide applicability of nonlocal modeling.
Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). First posted on arXiv 4 Feb 2020.
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Citing papers per year: 2021: 1; 2022: 5; 2023: 0; 2024: 2; 2025: 1; 2026: 3.
12 papers.
- 2022Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Networkcited by 45
- 2024Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operatorcited by 12
- 2022Nonparametric learning of kernels in nonlocal operatorscited by 10
- 2024Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networkscited by 5
- 2021A Caputo fractional derivative-based algorithm for optimizationcited by 3
- 2022Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditionscited by 3
- 2025Solving Nonlinear PDEs with Sparse Radial Basis Function Networkscited by 3
- 2022MC-Nonlocal-PINNs: handling nonlocal operators in PINNs via Monte Carlo samplingcited by 1
- 2022A Data-Adaptive Prior for Bayesian Learning of Kernels in Operatorscited by 1
- 2026Sparse RBF Networks for PDEs and nonlocal equations: function space theory, operator calculus, and training algorithmscited by 1
- 2026fTNN: a tensor neural network for fractional PDEscited by 0
- 2026Modeling Unknown Nonlocal PDE Systems via Flow Map Learningcited by 0
- 2026Modeling Unknown Nonlocal PDE Systems via Flow Map Learning1 Aug 2026
- 2026fTNN: a tensor neural network for fractional PDEs25 Jun 2026
- 2026Sparse RBF Networks for PDEs and nonlocal equations: function space theory, operator calculus, and training algorithms24 Jan 2026
- 2025Solving Nonlinear PDEs with Sparse Radial Basis Function Networks12 May 2025
- 2024Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operator6 Aug 2024
- 2024Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks17 Jun 2024
- 2022A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators29 Dec 2022
- 2022MC-Nonlocal-PINNs: handling nonlocal operators in PINNs via Monte Carlo sampling26 Dec 2022
- 2022Nonparametric learning of kernels in nonlocal operators23 May 2022
- 2022Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions7 May 2022
- 2022Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network6 Jan 2022
- 2021A Caputo fractional derivative-based algorithm for optimization6 Apr 2021