A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems | Paper Citation Record

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This paper cites Deep learning and process understanding for data-driven earth system science.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Deep learning and process understanding for data-driven earth system science

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A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Data-driven methods for flow and transport in porous media: A review

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This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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This paper cites Promising directions of machine learning for partial di fferential equations.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Promising directions of machine learning for partial di fferential equations

Reference 4

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A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Uncovering terrain-precipitation equation with inter- pretable AI: Towards future climate projection

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A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Genetic programming as a means for programming computers by natural selection

Reference 6

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A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Automated reverse engineering of nonlinear dynamical systems

Reference 7

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A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Distilling free-form natural laws from experimental data

Reference 8

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This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Reference 9

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This paper cites Data-driven discovery of partial di fferential equations.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Data-driven discovery of partial di fferential equations

Reference 10

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This paper cites Machine learning subsurface flow equations from data.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Machine learning subsurface flow equations from data

Reference 11

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This paper cites Weak SINDy for partial di fferential equations.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Weak SINDy for partial di fferential equations

Reference 12

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This paper cites Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control

Reference 13

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A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Robust data-driven dynamic model discovery of industrial robots with spatial manipulation capability using simple trajectory

Reference 14

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This paper cites Discovering governing equation from data for multi-stable energy harvester under white noise.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Discovering governing equation from data for multi-stable energy harvester under white noise

Reference 15

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This paper cites DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data

Reference 16

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This paper cites DeepMoD: Deep learning for model discovery in noisy data.Journal of Computational Physics, 428:109985, 2021.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems DeepMoD: Deep learning for model discovery in noisy data.Journal of Computational Physics, 428:109985, 2021

Reference 17

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This paper cites Integration of knowledge and data in machine learning.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Integration of knowledge and data in machine learning

Reference 18

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This paper cites Robust discovery of partial di fferential equations in complex situations.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Robust discovery of partial di fferential equations in complex situations

Reference 19

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This paper cites Metzler and J.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Metzler and J

Reference 20

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This paper cites Mechanisms, upscaling, and prediction of anomalous dispersion in heterogeneous porous media.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Mechanisms, upscaling, and prediction of anomalous dispersion in heterogeneous porous media

Reference 21

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This paper cites Data-driven identification of parametric partial di fferential equa- tions.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Data-driven identification of parametric partial di fferential equa- tions

Reference 22

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This paper cites Wheatcraft and Scott W Tyler.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Wheatcraft and Scott W Tyler

Reference 23

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This paper cites A review and numerical assessment of the random walk particle tracking method.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems A review and numerical assessment of the random walk particle tracking method

Reference 24

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This paper cites Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data

Reference 25

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Observation 95abf519-234c-4d3f-9598-d834a0db5d86 · outbound

This paper cites Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE).

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)

Reference 26

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This paper cites The data-driven discovery of partial di fferential equations by symbolic genetic algorithm.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems The data-driven discovery of partial di fferential equations by symbolic genetic algorithm

Reference 27

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This paper cites DISCOVER: Deep identification of symbolically concise open-form partial differential equations via enhanced reinforcement learning.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems DISCOVER: Deep identification of symbolically concise open-form partial differential equations via enhanced reinforcement learning

Reference 28

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This paper cites Physics-constrained robust learning of open-form partial differential equations from limited and noisy data.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Physics-constrained robust learning of open-form partial differential equations from limited and noisy data

Reference 29

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This paper cites LLM4ED: Large Language Models for Automatic Equation Discovery.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems LLM4ED: Large Language Models for Automatic Equation Discovery

Reference 30

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This paper cites Physics-informed deep neural networks for learning parameters and constitutive relationships in subsurface flow problems.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Physics-informed deep neural networks for learning parameters and constitutive relationships in subsurface flow problems

Reference 31

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This paper cites Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the glue methodology.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the glue methodology

Reference 32

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This paper cites Field study of dispersion in a heterogeneous aquifer: 2.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Field study of dispersion in a heterogeneous aquifer: 2

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This paper cites Cushman, Lynn S.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Cushman, Lynn S

Reference 34

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A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Unresolved cited work

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This paper cites Theory of solute transport by groundwater.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Theory of solute transport by groundwater

Reference 36

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This paper cites Modeling non-fickian transport in geological formations as a continuous time random walk.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Modeling non-fickian transport in geological formations as a continuous time random walk

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This paper cites Application of a fractional advection-dispersion equation.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Application of a fractional advection-dispersion equation

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This paper cites Multiple-rate mass transfer for modeling di ffusion and surface reactions in media with pore-scale heterogeneity.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Multiple-rate mass transfer for modeling di ffusion and surface reactions in media with pore-scale heterogeneity

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This paper cites Theory and applications of fractional di fferential equations, volume 204.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Theory and applications of fractional di fferential equations, volume 204

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This paper cites Podlubny.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Podlubny

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Observation a5bd4bd1-0edc-448c-99cd-c90b094d7648 · outbound

This paper cites Stochastic models for fractional calculus , volume 43.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Stochastic models for fractional calculus , volume 43

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.692283Z

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This paper cites Fractional partial differential equations and their numerical solutions.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Fractional partial differential equations and their numerical solutions

Reference 43

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Observation 24b4d95e-8069-41fe-9279-181cc4df9812 · outbound

This paper cites A new collection of real world applications of fractional calculus in science and engineering.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems A new collection of real world applications of fractional calculus in science and engineering

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.668216Z

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This paper cites A space fractional constitutive equation model for non-newtonian fluid flow.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems A space fractional constitutive equation model for non-newtonian fluid flow

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.656747Z

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Observation 93b7a8bc-b5c2-4bf0-8a83-3ca18ac2b9cf · outbound

This paper cites Generalized viscoelastic models: their fractional equations with solutions.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Generalized viscoelastic models: their fractional equations with solutions

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.645752Z

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This paper cites Fractional calculus and continuous-time finance.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Fractional calculus and continuous-time finance

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.634888Z

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Observation 20ab25c6-9f62-4b43-870e-ed14b6d1f1a0 · outbound

This paper cites Using gauss-jacobi quadrature rule to improve the accuracy of fem for spatial fractional problems.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Using gauss-jacobi quadrature rule to improve the accuracy of fem for spatial fractional problems

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.623874Z

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Observation a5f2659a-bb2e-447f-9891-57956695d568 · outbound

This paper cites fpinns: Fractional physics-informed neural networks.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems fpinns: Fractional physics-informed neural networks

Reference 49

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Observation bb1cb30d-6d78-4549-b7ba-cb9fc5dca64c · outbound

This paper cites Machine learning of space-fractional di fferential equations.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Machine learning of space-fractional di fferential equations

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.607156Z

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Observation aeff6f15-2c0c-456b-8d97-2bd25aad11f4 · outbound

This paper cites Coelho, M.Fernanda P.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Coelho, M.Fernanda P

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.596601Z

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Observation 97d29150-9e87-4724-a1fc-ad7d1ee725ae · outbound

This paper cites Neural fractional di fferential equations.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Neural fractional di fferential equations

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.586060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 587096d1-4346-4dc3-b654-8da883c9afce · outbound

This paper cites Data-driven discovery of time fractional di fferential equations.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Data-driven discovery of time fractional di fferential equations

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.575665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e57db67c-2fa2-492e-a036-8c8986148289 · outbound

This paper cites A new perspective for scientific modelling: Sparse reconstruction- based approach for learning time-space fractional di fferential equations.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems A new perspective for scientific modelling: Sparse reconstruction- based approach for learning time-space fractional di fferential equations

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.565491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 649da173-b66e-4dba-bd2e-f9a3389c2c63 · outbound

This paper cites Gauss-Jacobi-type quadrature rules for fractional directional integrals.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Gauss-Jacobi-type quadrature rules for fractional directional integrals

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.554881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5928539a-984e-4a80-8a3d-9170d68f5ad0 · outbound

This paper cites Physics-informed learning of governing equations from scarce data.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Physics-informed learning of governing equations from scarce data

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.544633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5954f211-4e62-415e-af82-bcaeb702887a · outbound

This paper cites Discovery of subdi ffusion problem with noisy data via deep learning.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Discovery of subdi ffusion problem with noisy data via deep learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.533200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 12c2c564-6502-4da6-a6f5-4f1521008dbe · outbound

This paper cites Deep hidden physics models: Deep learning of nonlinear partial di fferential equations.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Deep hidden physics models: Deep learning of nonlinear partial di fferential equations

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.523249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 07950fce-a5b3-4123-92e2-99e3298b3a09 · outbound

This paper cites Particle swarm fractional order derivative model of artificial frozen soil creep properties.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Particle swarm fractional order derivative model of artificial frozen soil creep properties

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.512637Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e6425978-7d01-44f3-a804-55f35a15c759 · outbound

This paper cites Time and space nonlocalities underlying fractional-derivative models: Distinction and literature review of field applications.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Time and space nonlocalities underlying fractional-derivative models: Distinction and literature review of field applications

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.501900Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f3cf89fd-eb96-4b24-ad7d-b455e725de00 · outbound

This paper cites Data-driven discovery of governing equations for fluid dynamics based on molecular simulation.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Data-driven discovery of governing equations for fluid dynamics based on molecular simulation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.491366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 015452fb-1faa-4f46-8ecc-803abe187306 · outbound

This paper cites Limit distributions for sums of independent random variables, volume 233.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Limit distributions for sums of independent random variables, volume 233

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.480210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a06509d7-e3aa-4513-88f2-cf5afd8971cf · outbound

This paper cites Fractional dispersion, Lévy motion, and the made tracer tests.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Fractional dispersion, Lévy motion, and the made tracer tests

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.468418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5d6e75f4-ac2a-4492-842e-5a364a925d91 · outbound

This paper cites Théorie de l’addition des variables aléatoires.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Théorie de l’addition des variables aléatoires

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.456076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 55608b7c-ca4b-47da-aab4-f151d11a380b · outbound

This paper cites DLGA-PDE: Discovery of pdes with incomplete candidate library via combination of deep learning and genetic algorithm.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems DLGA-PDE: Discovery of pdes with incomplete candidate library via combination of deep learning and genetic algorithm

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.444090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ac1111f5-96d2-4db6-a375-d15f82b2c542 · outbound

This paper cites Neural fractional di fferential equations: Optimising the order of the fractional 19 derivative.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Neural fractional di fferential equations: Optimising the order of the fractional 19 derivative

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.431430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 07135f55-a528-4a42-938d-230da5dd8c51 · outbound

This paper cites Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T21:58:12.169386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7166941-0c1c-4705-8b64-e4e44d87e549 · outbound

This paper cites Fde-net: A memory-e fficiency densely connected network inspired from fractional-order differential equations for single image super-resolution.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Fde-net: A memory-e fficiency densely connected network inspired from fractional-order differential equations for single image super-resolution

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.419400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e5ee93ae-bb99-49af-befb-e1ad7160f2f7 · outbound

This paper cites Optimising neural fractional di fferential equations for performance and efficiency.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Optimising neural fractional di fferential equations for performance and efficiency

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.406353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8573b63d-2614-4519-b3e8-076a36a1348a · outbound

This paper cites Neural variable-order fractional differential equation networks.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Neural variable-order fractional differential equation networks

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T21:58:12.180396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f41bb798-71c3-42bc-88c8-6291dcd05b1d · outbound

This paper cites Efficient training of neural fractional-order differential equation via adjoint backpropagation.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Efficient training of neural fractional-order differential equation via adjoint backpropagation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.388178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b782da2b-dc64-4e80-92ab-b05cd45b0554 · outbound

This paper cites Spectral methods: algorithms, analysis and applications, volume 41.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Spectral methods: algorithms, analysis and applications, volume 41

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.376788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 13d8e715-2932-4785-8902-4a8c73be669c · outbound

This paper cites Theory and Numerical Approximations of Fractional Integrals and Derivatives.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Theory and Numerical Approximations of Fractional Integrals and Derivatives

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.365652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2a1fad19-346e-4807-a491-7b0bc4f70a8f · outbound

This paper cites Calculation of gauss quadrature rules.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Calculation of gauss quadrature rules

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T21:58:12.194597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0e573d1-b8b2-486d-8d68-617212393c5f · outbound

This paper cites Lin and C.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Lin and C

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.346614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 03e64fb9-4857-40a7-8091-3f9a3161e7b9 · outbound

This paper cites An introduction to probability theory and its applications, Volume 2 , volume 81.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems An introduction to probability theory and its applications, Volume 2 , volume 81

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.334755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b8d8a7e0-fa66-4973-b394-9a00cf147555 · outbound

This paper cites On using random walks to solve the space-fractional advection-dispersion equations.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems On using random walks to solve the space-fractional advection-dispersion equations

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.323080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8c0a8211-1377-4487-a96e-d05454a125fd · outbound

This paper cites Parameterizations and modes of stable distributions.

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems Parameterizations and modes of stable distributions

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:58:12.311686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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