Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches | Paper Citation Record

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This paper cites Force variation within arrays of monodisperse spherical particles.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Force variation within arrays of monodisperse spherical particles

Reference 1

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This paper cites Pairwise interaction extended point-particle model for a random array of monodisperse spheres.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Pairwise interaction extended point-particle model for a random array of monodisperse spheres

Reference 2

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This paper cites Pairwise-interaction extended point-particle model for particle-laden flows.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Pairwise-interaction extended point-particle model for particle-laden flows

Reference 3

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This paper cites Symbolic regression based hybrid semiparametric modelling of processes: An example case of a bending process.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Symbolic regression based hybrid semiparametric modelling of processes: An example case of a bending process

Reference 4

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Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Unresolved cited work

Reference 5

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Observation 2b5c0991-f10f-4bcc-babb-ff3c862c5801 · outbound

This paper cites Theoretical and Computational Fluid Dynamics doi:10.1007/s00162-020-00538-8.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Theoretical and Computational Fluid Dynamics doi:10.1007/s00162-020-00538-8

Reference 6

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Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Unresolved cited work

Reference 7

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This paper cites Relational inductive biases, deep learning, and graph networks.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Relational inductive biases, deep learning, and graph networks

Reference 8

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This paper cites Interaction Networks for Learning about Objects, Relations and Physics.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Interaction Networks for Learning about Objects, Relations and Physics

Reference 9

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This paper cites Drag force of intermediate Reynolds number flow past mono-and bidisperse arrays of spheres.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Drag force of intermediate Reynolds number flow past mono-and bidisperse arrays of spheres

Reference 10

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Observation 010e9679-c8c8-4fd8-b331-3b604045a00e · outbound

This paper cites Geometric Deep Learning: Going beyond Euclidean data.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Geometric Deep Learning: Going beyond Euclidean data

Reference 11

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This paper cites Machine Learning for Partial Differential Equations.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Machine Learning for Partial Differential Equations

Reference 12

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Observation 74c4f979-d050-4e44-bd83-c9422ac8e605 · outbound

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

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Reference 13

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This paper cites Operon C++: an efficient genetic programming framework for symbolicregression,in:Proceedingsofthe2020GeneticandEvolutionaryComputationConferenceCompanion,ACM, Cancún Mexico.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Operon C++: an efficient genetic programming framework for symbolicregression,in:Proceedingsofthe2020GeneticandEvolutionaryComputationConferenceCompanion,ACM, Cancún Mexico

Reference 14

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This paper cites Solving symbolic regression problems with formal constraints, in: Proceedings of the Genetic and Evolutionary Computation Conference, ACM, Prague Czech Republic.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Solving symbolic regression problems with formal constraints, in: Proceedings of the Genetic and Evolutionary Computation Conference, ACM, Prague Czech Republic

Reference 15

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This paper cites A hybrid immersed boundary method for dense particle-laden flows.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches A hybrid immersed boundary method for dense particle-laden flows

Reference 16

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This paper cites Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Reference 17

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This paper cites Discovering Symbolic Models from Deep Learning with Inductive Biases, in: Advances in Neural Information Processing Sys- tems, Curran Associates, Inc.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Discovering Symbolic Models from Deep Learning with Inductive Biases, in: Advances in Neural Information Processing Sys- tems, Curran Associates, Inc

Reference 18

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Observation 16873455-a15b-4824-bd99-65c285dcc5a1 · outbound

This paper cites Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

Reference 19

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Observation 2b30e216-f4fc-46a1-a6b1-ffc6ea7f6aad · outbound

This paper cites ODEFormer: Symbolic Regression of Dynamical Systems with Transformers.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

Reference 20

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Observation 7db51314-0fbe-44d5-8e01-a54e3ffe03ab · outbound

This paper cites Discovery of Physics From Data: Universal Laws and Discrepancies.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Discovery of Physics From Data: Universal Laws and Discrepancies

Reference 21

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This paper cites A fast and elitist multiobjective genetic algorithm: NSGA-II.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches A fast and elitist multiobjective genetic algorithm: NSGA-II

Reference 22

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This paper cites Conservative finite-volume framework and pressure-based algorithm for flows of incompressible, ideal-gas and real-gas fluids at all speeds.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Conservative finite-volume framework and pressure-based algorithm for flows of incompressible, ideal-gas and real-gas fluids at all speeds

Reference 23

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Observation bdd3012f-eee3-4efc-95e3-83f00aa4edce · outbound

This paper cites Deterministic drag modelling for spherical particles in Stokes regime using data-driven approaches.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Deterministic drag modelling for spherical particles in Stokes regime using data-driven approaches

Reference 24

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This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Fast Graph Representation Learning with PyTorch Geometric

Reference 25

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This paper cites Alleviating overfitting in transformation-interaction-rational symbolic regression with multi- objective optimization.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Alleviating overfitting in transformation-interaction-rational symbolic regression with multi- objective optimization

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This paper cites Shape-constrained multi-objective genetic programming for symbolic regression.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Shape-constrained multi-objective genetic programming for symbolic regression

Reference 27

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Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Unresolved cited work

Reference 28

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This paper cites Shape-ConstrainedSymbolicRegressionwithNSGA-III,in:Moreno-Díaz,R.,Pichler, F., Quesada-Arencibia, A.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Shape-ConstrainedSymbolicRegressionwithNSGA-III,in:Moreno-Díaz,R.,Pichler, F., Quesada-Arencibia, A

Reference 29

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This paper cites A supervised machine learning approach for predicting variable drag forces on spherical particles in suspension.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches A supervised machine learning approach for predicting variable drag forces on spherical particles in suspension

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This paper cites Deep Generative Symbolic Regression.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Deep Generative Symbolic Regression

Reference 31

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This paper cites End-to-end Symbolic Regression with Transformers, in: Koyejo, S.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches End-to-end Symbolic Regression with Transformers, in: Koyejo, S

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Observation 44900bac-ebb0-4907-a666-ea58a84e6da4 · outbound

This paper cites Physics-informedmachinelearning.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Physics-informedmachinelearning

Reference 33

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This paper cites Dimensionally Aware Genetic Programming, in: GECCO’99: Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Dimensionally Aware Genetic Programming, in: GECCO’99: Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation

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This paper cites Characterizing possible failure modes in physics-informed neural networks.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Characterizing possible failure modes in physics-informed neural networks

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This paper cites Shape-Constrained Symbolic Regression—Improving Extrapolation with Prior Knowledge.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Shape-Constrained Symbolic Regression—Improving Extrapolation with Prior Knowledge

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This paper cites Symbolic regression driven by training data and prior knowledge, in: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, ACM, Cancún Mexico.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Symbolic regression driven by training data and prior knowledge, in: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, ACM, Cancún Mexico

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This paper cites Rediscovering orbital mechanics with machine learning.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Rediscovering orbital mechanics with machine learning

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This paper cites Dimensionally Aware Multi-Objective Genetic Programming for Automatic Crowd Behavior Modeling.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Dimensionally Aware Multi-Objective Genetic Programming for Automatic Crowd Behavior Modeling

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This paper cites MetaSymNet: A Tree-like Symbol Network with Adaptive Architecture and Activation Functions.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches MetaSymNet: A Tree-like Symbol Network with Adaptive Architecture and Activation Functions

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This paper cites Graphneuralnetwork-acceleratedLagrangianfluidsimulation.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Graphneuralnetwork-acceleratedLagrangianfluidsimulation

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This paper cites Introducing Thermodynamics-Informed Symbolic Regression -- A Tool for Thermodynamic Equations of State Development.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Introducing Thermodynamics-Informed Symbolic Regression -- A Tool for Thermodynamic Equations of State Development

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Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Unresolved cited work

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Observation 5e924621-1521-460c-8c31-6a6f3a0bf7bb · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches PyTorch: An Imperative Style, High-Performance Deep Learning Library

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This paper cites Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients

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Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Unresolved cited work

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Observation 2402bcd9-799a-499a-be94-c1dc88bf3c01 · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solvingforwardandinverseproblemsinvolvingnonlinearpartialdifferentialequations.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Physics-informed neural networks: A deep learning framework for solvingforwardandinverseproblemsinvolvingnonlinearpartialdifferentialequations

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This paper cites Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

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This paper cites Towards Improving Simulations of Flows around Spherical Particles Using Genetic Programming, in: 2022 IEEE Congress on Evolutionary Computation (CEC), pp.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Towards Improving Simulations of Flows around Spherical Particles Using Genetic Programming, in: 2022 IEEE Congress on Evolutionary Computation (CEC), pp

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This paper cites Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows, Springer Nature Switzerland, Cham.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows, Springer Nature Switzerland, Cham

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Resolution: verified exact; doi, observed 2026-08-06T19:20:48.571317Z

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Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Unresolved cited work

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This paper cites Sedimentation and fluidisation: Part I.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Sedimentation and fluidisation: Part I

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Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Unresolved cited work

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Observation 0b240076-3767-4425-b50f-7fdd72910282 · outbound

This paper cites Learning to Simulate Complex Physics with Graph Networks.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Learning to Simulate Complex Physics with Graph Networks

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Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Unresolved cited work

Reference 55

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This paper cites Improving Expert Knowledge in Dynamic Process Monitoring by Symbolic Regression,in:2012SixthInternationalConferenceonGeneticandEvolutionaryComputing,IEEE,Kitakyushu,Japan.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Improving Expert Knowledge in Dynamic Process Monitoring by Symbolic Regression,in:2012SixthInternationalConferenceonGeneticandEvolutionaryComputing,IEEE,Kitakyushu,Japan

Reference 56

Resolution: verified exact; doi, observed 2026-08-06T19:20:47.637307Z

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This paper cites Microstructure-informedprobability-drivenpoint-particlemodelforhydrodynamic forces and torques in particle-laden flows.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Microstructure-informedprobability-drivenpoint-particlemodelforhydrodynamic forces and torques in particle-laden flows

Reference 57

Resolution: verified exact; doi, observed 2026-08-06T19:20:47.308458Z

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Observation 87890c1c-32fd-40e6-94ca-d158534c25e0 · outbound

This paper cites Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows

Reference 58

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Observation 0d3c04d5-f9dc-4e96-98fe-22a669311dde · outbound

This paper cites Point-particle drag, lift, and torque closure models using machine learning: Hierarchical approach and interpretability.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Point-particle drag, lift, and torque closure models using machine learning: Hierarchical approach and interpretability

Reference 59

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This paper cites Investigatingtheinfluenceofparticledistributiononforce and torque statistics using hierarchical machine learning.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Investigatingtheinfluenceofparticledistributiononforce and torque statistics using hierarchical machine learning

Reference 60

Resolution: verified exact; doi, observed 2026-08-06T19:20:47.046670Z

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This paper cites A new drag correlation from fully resolved simulations of flow past monodisperse static arrays of spheres.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches A new drag correlation from fully resolved simulations of flow past monodisperse static arrays of spheres

Reference 61

Resolution: verified exact; doi, observed 2026-08-06T19:20:46.718510Z

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Observation 478e41a2-6ab8-4c55-8014-e40409ca4640 · outbound

This paper cites Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws

Reference 62

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This paper cites Draglawformonodispersegas–solidsystemsusingparticle-resolveddirect numericalsimulationof flowpastfixedassembliesofspheres.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Draglawformonodispersegas–solidsystemsusingparticle-resolveddirect numericalsimulationof flowpastfixedassembliesofspheres

Reference 63

Resolution: verified exact; doi, observed 2026-08-06T19:20:46.289349Z

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Observation 22a533af-a782-4d9f-be51-7b603bbb756b · outbound

This paper cites AI Feynman 2.0: Pareto- optimal symbolic regression exploiting graph modularity, in: Larochelle, H., Ranzato, M., Hadsell, R., Bal- can, M., Lin, H.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches AI Feynman 2.0: Pareto- optimal symbolic regression exploiting graph modularity, in: Larochelle, H., Ranzato, M., Hadsell, R., Bal- can, M., Lin, H

Reference 64

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Observation 81c28cd1-0f50-49c3-a2b8-7878cdc492c2 · outbound

This paper cites AIFeynman:Aphysics-inspiredmethodforsymbolicregression.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches AIFeynman:Aphysics-inspiredmethodforsymbolicregression

Reference 65

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This paper cites Microstructure-based prediction of hydrodynamic forces in stationary particle assemblies.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Microstructure-based prediction of hydrodynamic forces in stationary particle assemblies

Reference 66

Resolution: verified exact; doi, observed 2026-08-06T19:20:46.154631Z

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Observation fe595924-4da5-43d6-a3c0-2d48ce340ec9 · outbound

This paper cites Microstructure-basedpredictionofhydrodynamicforcesinstation- ary particle assemblies.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Microstructure-basedpredictionofhydrodynamicforcesinstation- ary particle assemblies

Reference 67

Resolution: malformed identifier; no resolver link, observed 2026-08-06T19:20:45.554558Z

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Observation f6ff53f0-1e32-4f41-a7b0-bc31842a65a8 · outbound

This paper cites Data driven modeling of plastic deformation.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Data driven modeling of plastic deformation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T19:20:45.644675Z

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Observation fa39468d-74cf-4541-b948-a5b8fa234a60 · outbound

This paper cites Machine Learning the Gravity Equation for International Trade.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Machine Learning the Gravity Equation for International Trade

Reference 69

Resolution: verified exact; doi, observed 2026-08-06T19:20:45.968973Z

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Observation a9b51b42-bb4f-4c10-86eb-08250cea6953 · outbound

This paper cites Machine Learning with Physics Knowledge for Prediction: A Survey.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Machine Learning with Physics Knowledge for Prediction: A Survey

Reference 70

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Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Unresolved cited work

Reference 71

Resolution: verified exact; raw_fallback, observed 2026-08-06T19:20:50.752029Z

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Observation 3a14d58e-94f8-400a-ae74-4e656c6ca34c · outbound

This paper cites an unresolved cited work.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Unresolved cited work

Reference 2024

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