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REVIEW 3 major objections 2 minor 74 references

Regularized Machine Learning for System Identification of Ship Free-Running Manoeuvres from CFD-Based Synthetic Data: A Comparative Study

T0 review · 3 major / 2 minor · reviewed 2026-06-27 · grok-4.3

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Pith's one-line read Regularized regression techniques, especially Ridge, enhance the accuracy of identifying ship hydrodynamic coefficients from CFD data by addressing multicollinearity.

desk verdict A straightforward comparative study applying standard regularized regression to Abkowitz models on CFD ship data; useful checks on manoeuvres and coefficient sets but no big surprises or quantitative detail. read the letter →

arxiv 2606.17121 v1 pith:2FO4OJJP submitted 2026-06-15 stat.AP cs.LGphysics.flu-dyn

R.F. Suárez , J.C. Berndt , M. Abdel-Maksoud This is my paper · ORCID
classification stat.APcs.LGphysics.flu-dyn
keywords systemidentificationregularizedregressionhydrodynamiccoefficientsshipmanoeuvringCFDsimulationsmachinelearningmulticollinearity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper examines the use of supervised machine learning, including ordinary least squares and regularized regressions, to extract hydrodynamic coefficients for ship manoeuvring models from synthetic CFD data. The data comes from simulations of zig-zag and turning circle manoeuvres validated against experiments. Results indicate that regularization mitigates multicollinearity problems, particularly with larger coefficient sets, and improves prediction performance. Diverse manoeuvre data further aids accuracy, with Ridge regression striking the best balance between speed and reliability.

What carries the argument

Regularized linear regression (Ridge, Lasso, Elastic Net) on Abkowitz-type coefficient models trained on CFD-derived time series from zig-zag and turning tests.

What would settle it

A direct comparison showing that models trained on this CFD data fail to predict manoeuvres accurately when tested against independent experimental sea trials would falsify the suitability of the data.

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Extended reading notes

Core claim

Regularized regression methods applied to Abkowitz-type manoeuvring models from URANS simulations of free-running manoeuvres mitigate multicollinearity and improve prediction accuracy over ordinary least squares, with Ridge regression offering the best efficiency-accuracy compromise.

Load-bearing premise

The URANS CFD simulations of the manoeuvres provide data that sufficiently represents the ship's true hydrodynamic behaviour for coefficient identification.

Editorial extensions

If this is right

  • Larger sets of coefficients allow greater model flexibility but increase multicollinearity risk unless regularized.
  • Large-angle zig-zag manoeuvres suffice for identification when multicollinearity is controlled via regression choice or data variety.
  • Including more varied manoeuvring data boosts overall model performance.
  • Ridge regression balances computational efficiency and accuracy better than other tested regularizers.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The approach could be extended to real experimental data to bridge simulation and physical tests.
  • Similar techniques might apply to identifying models for other dynamic systems like aircraft or underwater vehicles.
  • Minimum training lengths identified could guide efficient data collection in future studies.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The paper investigates supervised machine learning for identifying ship hydrodynamic coefficients from CFD-generated data of free-running manoeuvres. It applies ordinary least squares and regularized regression (Ridge, etc.) to Abkowitz-type models using URANS simulations of zig-zag and turning-circle manoeuvres that are validated against experimental benchmarks. The study examines effects of coefficient-set size, minimum training length, and manoeuvre combinations, concluding that regularization mitigates multicollinearity, enhances prediction accuracy, and that Ridge regression offers the best efficiency-accuracy trade-off while larger, more diverse datasets improve results.

Significance. If the quantitative results hold under scrutiny, the work offers a practical empirical demonstration of regularization benefits for handling multicollinear regressors in marine system identification from synthetic CFD data. It provides guidance on manoeuvre selection (e.g., large-angle zig-zag) and data diversity for coefficient estimation, which could aid practitioners using simulation-based identification. The comparative evaluation across regression methods and data configurations is a useful contribution to the applied statistics and naval hydrodynamics literature, though its impact depends on the fidelity of the underlying CFD force data.

major comments (3)
  1. [Abstract / Validation] Abstract and validation section: the claim that URANS simulations are validated against experimental benchmarks is load-bearing for all downstream regression results, yet the abstract provides no quantitative metrics (e.g., trajectory RMSE, force time-history correlation, or regressor-matrix condition numbers) to confirm that point-wise hydrodynamic forces are faithfully reproduced rather than only global manoeuvring paths.
  2. [Results] Results section on regularization: the assertion that 'regularized regression techniques effectively mitigate multicollinearity and notably enhance prediction accuracy' requires explicit reporting of effect sizes (e.g., changes in condition number, cross-validation RMSE, or R² with and without regularization) together with error bars or statistical significance tests; without these, the magnitude and robustness of the claimed improvement cannot be assessed.
  3. [Discussion / Methods] Discussion of CFD data: because all comparisons (OLS vs. Ridge, coefficient-set sizes, manoeuvre diversity) are performed within the same URANS framework, any systematic bias in the synthetic force time series (numerical dissipation, turbulence modeling, or free-surface effects correlated with regressors) would propagate identically to all methods and could artifactually favor regularization; a sensitivity test against experimental force data or perturbed CFD runs is needed to establish that the reported superiority is not data-specific.
minor comments (2)
  1. [Methods] Clarify the exact Abkowitz model structure and which coefficients are included in each 'set size' tested.
  2. [Results] Provide the precise definition of 'prediction accuracy' (e.g., one-step-ahead vs. multi-step trajectory error) and the validation split used.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive and detailed comments, which help strengthen the manuscript. We address each major comment below, indicating where revisions will be made.

read point-by-point responses

  1. Referee: [Abstract / Validation] Abstract and validation section: the claim that URANS simulations are validated against experimental benchmarks is load-bearing for all downstream regression results, yet the abstract provides no quantitative metrics (e.g., trajectory RMSE, force time-history correlation, or regressor-matrix condition numbers) to confirm that point-wise hydrodynamic forces are faithfully reproduced rather than only global manoeuvring paths.

    Authors: We agree that the abstract should better substantiate the validation claim. The manuscript's validation section reports quantitative trajectory metrics (e.g., RMSE for heading and position time histories) against experimental benchmarks, but direct point-wise force correlations are not available as the CFD validation emphasized global paths. We will revise the abstract to include key trajectory RMSE values and condition number references from the results, while clarifying the scope of validation. revision: yes

  2. Referee: [Results] Results section on regularization: the assertion that 'regularized regression techniques effectively mitigate multicollinearity and notably enhance prediction accuracy' requires explicit reporting of effect sizes (e.g., changes in condition number, cross-validation RMSE, or R² with and without regularization) together with error bars or statistical significance tests; without these, the magnitude and robustness of the claimed improvement cannot be assessed.

    Authors: We concur that explicit effect sizes and robustness measures are needed. The current results compare prediction RMSE and condition numbers across methods, but we will expand the section to report delta values (e.g., condition number reduction, CV-RMSE differences with standard deviations), R² changes, and error bars from repeated cross-validation folds to quantify the improvements. revision: yes

  3. Referee: [Discussion / Methods] Discussion of CFD data: because all comparisons (OLS vs. Ridge, coefficient-set sizes, manoeuvre diversity) are performed within the same URANS framework, any systematic bias in the synthetic force time series (numerical dissipation, turbulence modeling, or free-surface effects correlated with regressors) would propagate identically to all methods and could artifactually favor regularization; a sensitivity test against experimental force data or perturbed CFD runs is needed to establish that the reported superiority is not data-specific.

    Authors: This concern about potential data-specific bias is well-taken. All methods are evaluated on identical synthetic data, so relative rankings remain valid for the comparative purpose, but absolute claims could be affected by CFD artifacts. Direct experimental force time series are unavailable in the dataset (validation was trajectory-based), precluding a full sensitivity test here. We will add an explicit limitations paragraph discussing this scope and the value of future perturbed-CFD or hybrid experimental-CFD studies. revision: partial

standing simulated objections not resolved
  • Full sensitivity testing against experimental force time-series data, as such direct force measurements are not available in the current CFD-validated trajectory dataset.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical regression comparison on external CFD data

full rationale

The paper conducts an empirical comparison of OLS and regularized regression (Ridge, etc.) applied to standard Abkowitz-type models using time-series from URANS free-running simulations of zig-zag and turning-circle manoeuvres. Training/validation splits, coefficient-set sizes, and manoeuvre diversity are varied, with performance measured on held-out segments; no equations, uniqueness theorems, or self-citations are invoked to derive results by construction from the fitted parameters themselves. The reported superiority of regularization follows directly from standard statistical properties evaluated on the provided synthetic data, making the derivation self-contained against external benchmarks.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

The central claim rests on the validity of the Abkowitz-type model structure and the representativeness of the CFD data; no free parameters, axioms, or invented entities are explicitly introduced or quantified in the abstract.

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Cite this review

Pith. "Pith review of Regularized Machine Learning for System Identification of Ship Free-Running Manoeuvres from CFD-Based Synthetic Data: A Comparative Study." pith.science (2026). https://pith.science/paper/2FO4OJJP

@misc{pith2026260617121,
  author       = {Pith},
  title        = {Pith review of: Regularized Machine Learning for System Identification of Ship Free-Running Manoeuvres from CFD-Based Synthetic Data: A Comparative Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2FO4OJJP}},
  note         = {Machine review of arXiv:2606.17121}
}
read the original abstract

This study investigates supervised machine learning techniques for identifying ship hydrodynamic coefficients from CFD-generated data from free-running simulations. Specifically, ordinary least squares and regularized regression methods are applied to Abkowitz-type manoeuvring models. Training and validation datasets are derived from URANS simulations of zig-zag and turning circle manoeuvres, which are validated against experimental benchmark data. The analysis evaluates the effects of coefficient set size, minimum training length required for predictive model training, and manoeuvre combinations on model performance. Results demonstrate the suitability of large-angle zig-zag manoeuvres for hydrodynamic system identification, provided that multicollinearity is addressed through appropriate coefficient selection, regression models, or input data variability. Larger coefficient sets offer greater model flexibility for variable conditions but are more prone to multicollinearity. Regularized regression techniques effectively mitigate multicollinearity and notably enhance prediction accuracy, as does incorporating more diverse manoeuvring data. Among tested models, Ridge regression provided the best compromise between computational efficiency and prediction accuracy.

Figures

Figures reproduced from arXiv: 2606.17121 by the authors.

Figure 2
Figure 2. CFD grid of overset hull region [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. CFD grid of full computational domain Prism layers on the hull surface ensure a non-dimensional wall distance 𝑦 + averaged over the submerged part of the hull surface of 30 to 60. Wall functions are used to model the near-wall flow. At the boundaries in the horizontal direction (back, PSside, SBside and front in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Forcing zone blending for a manoeuvring ship with moving domain To reduce the computational effort of the simulations, the propeller is modelled with the STAR-CCM+ actuator disk model, which computes the influence of the propeller on the fluid field and the forces on the ship as a function of the propeller rotation rate and the velocity field in the propeller inflow plane. The propeller inflow plane is placed 10% of… view at source ↗
Figures from the paper (17 more)
Figure 5
Figure 5. Figure 5: KVLCC2 hull geometry and ship-fixed coordinate system The numerical flow simulations are performed in model scale, while further investigations are conducted using non￾dimensional variables. x y z U u v -β δ [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Coordinate system [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: CFD simulation trajectories for the ZZ 10◦/10◦and ZZ 20◦/20◦manoeuvres starting to PS 0 10 20 30 40 50 t * [-] 40 20 0 20 angle [°] CFD CFD SIMMAN2014 FRMT SIMMAN2014 FRMT [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 9
Figure 9. Figure 9: presents results of the large angle zig-zag ma￾noeuvres ZZ 35◦ /35◦ PS and SB. These simulations were performed to generate manoeuvring time series data that contains a lot of dynamic information for the extraction of hydrodynamic coefficients. Both simulations resulte…
Figure 10
Figure 10. Figure 10: CFD simulation trajectories fr the TC 35◦ manoeu￾vre starting to PS and SB [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: Predicted Trajectories of the input manoeuvre (a) and a test manoeuvre (b) in comparison with CFD simulation data for the full set of coefficients given in Crane et al. (1988) and the reduced set, suggested for the KVLCC2 by Mucha (2017) larger coefficient set are unr…
Figure 14
Figure 14. Figure 14: VIF values for surge coefficients (𝑋) for CFD simulation data for the full set of coefficients given in Crane et al. (1988) and the reduced set, suggested by Mucha (2017) (285.2%), and Steady Diameter (433.3%), which indicates poor out-of-sample robustness under sever…
Figure 17
Figure 17. Figure 17: Predicted Trajectories of the input manoeuvre (a) and a test manoeuvre (b) in comparison with CFD simulation data for coefficients predicted with time series ending with 1 to 8 overshoots estimators are compared under identical pre-processing and the same design matri…
Figure 16
Figure 16. Figure 16: Hydrodynamic coefficients predicted from the ZZ 35◦/35◦ PS manoeuvre as a function of the time series length used (until 1 to 8 overshoots) (3.6%, Tab. 8) under the consistent averaging definition stated above, and (iii) keeps in-sample ZZ 35◦∕35◦ PS errors low (avera…
Figure 18
Figure 18. Figure 18: Non-dimensional surge-force coefficients (𝑋) for different predictor models (LSQ, Lasso, Ridge, SVR). Pink shading highlights coefficients with inconsistent signs or zero magnitude. Vertical axis is logarithmic. (a) LSQ (larger vertical axis limits!) (b) Lasso (c) Rid…
Figure 20
Figure 20. Figure 20: Non-dimensional sway-force coefficients (𝑌 ) for different predictor models (LSQ, Lasso, Ridge, SVR). Pink shading highlights coefficients with inconsistent signs or zero magnitude. Vertical axis is logarithmic. (a) LSQ (larger vertical axis limits!) (b) Lasso (c) Rid…
Figure 22
Figure 22. Figure 22: Non-dimensional yaw-moment coefficients (𝑁) for different predictor models (LSQ, Lasso, Ridge, SVR). Pink shading highlights coefficients with inconsistent signs or zero magnitude. Vertical axis is logarithmic. (a) LSQ (larger vertical axis limits!) (b) Lasso (c) Ridg…
Figure 24
Figure 24. Figure 24: Non-dimensional trajectories predicted by LSQ, Lasso, Ridge, and SVR models trained on a single ZZ 35◦∕35◦ (PS) manoeuvre using the large set of coefficients whereas the regularised models track the CFD trends more closely, consistent with their more compact force–mom…
Figure 25
Figure 25. Figure 25: Turning circle 35◦ : non-dimensional time histories for 𝑢, 𝑣, 𝑟 and 𝑋, 𝑌 , 𝑁. Black lines denote CFD references; coloured lines are model predictions [PITH_FULL_IMAGE:figures/full_fig_p023_25.png]
Figure 26
Figure 26. Figure 26: Predicted trajectories of input manoeuvre and test manoeuvres with predicted hydrodynamic coefficients using LSQ with several input manoeuvres in combination with the smaller set of coefficients from Mucha (2017); Black: CFD; coloured: model. identification data (gree…
Figure 27
Figure 27. Figure 27: As [PITH_FULL_IMAGE:figures/full_fig_p025_27.png]
Figure 28
Figure 28. Figure 28: As [PITH_FULL_IMAGE:figures/full_fig_p026_28.png]

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Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.