Pith. sign in

REVIEW 4 major objections 5 minor 34 references

Interpretable SHAP-bounded Bayesian Optimization for Underwater Acoustic Metamaterial Coating Design

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read SHAP-bounded Bayesian optimization matches classical BO in under a third of the iterations.

desk verdict A clear, honest description of a simple idea—use SHAP sign patterns to shrink BO bounds—but all reported gains are computed on a DNN surrogate, so the physical improvements are unproven. read the letter →

arxiv 2505.06519 v1 pith:NM7DXQCW submitted 2025-05-10 cs.LG cond-mat.mtrl-scics.SD

Hansani Weeratunge , Dominic Robe , Elnaz Hajizadeh This is my paper · ORCID
classification cs.LGcond-mat.mtrl-scics.SD
keywords BayesianoptimizationSHAPinterpretablemachinelearningunderwateracousticmetamaterialssoundabsorptiondomainreductioninversematerialdesigndeepneuralnetworksurrogate
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 claims that feeding SHAP interpretability analyses into Bayesian optimization can speed up and improve the optimization of underwater acoustic metamaterial coatings. The idea is to use the sign of SHAP values to tighten the allowed ranges of the most influential design variables, focusing the search where absorption is likely to be high. On two polyurethane materials, the authors report that the SHAP-informed routine matches a 400-iteration classical Bayesian optimization in fewer than a third of the iterations, and finishes the full budget with about 3% and 11% better objective values. The practical motivation is that each design evaluation is expensive, so any trustworthy way to reduce the search space matters.

What carries the argument

The central mechanism is the SHAP-guided bound-refinement loop. After an initial batch of 10 x D random samples, a deep neural network is trained and SHAP values are computed; for the top six features, if the best-so-far design has a positive SHAP value and all SHAP values to the right (or left) are positive, the lower (or upper) bound is moved to within 10% of the best-so-far value. The Bayesian optimizer then runs inside the tightened box using a Gaussian process surrogate and an expected-improvement acquisition function, and the loop repeats at iterations 100, 150, and 200. The SHAP analysis supplies large-scale trend information that a Gaussian process with a stationary kernel tends to miss, while BO supplies the local search.

What would settle it

Run the final SHAP-informed and standard-BO designs for both PU80 and PU90 through the original finite element model (or physical samples in an impedance tube) and compare the weighted absorption objective. If the surrogate's predicted objective differs materially from the FEM result, or if the SHAP-informed design is not better than standard BO under FEM evaluation, the claimed 3% and 11% improvements do not hold in the physical problem.

Watch

Extended reading notes

Core claim

The central claim is that SHAP-bounded Bayesian optimization, where SHAP values computed from a deep neural network surrogate are used to automatically tighten the design-space bounds, accelerates Bayesian optimization and yields better final designs than standard Bayesian optimization and a generic domain-reduction baseline. Applied to the ten-dimensional design of polyurethane coatings with embedded voids for underwater sound absorption, the method identifies the most influential geometric parameters, excludes regions with consistently negative SHAP values, and repeatedly re-bounds the search every 50 iterations after the 100th. In repeated runs with varied initial samples, SHAP-informed BO matched classical BO's 400-iteration result within about 130 iterations for both PU80 and PU90, and by the end of the same budget achieved on average 3% (PU80) and 11% (PU90) higher objective values. The authors present this as the first algorithmic integration of SHAP into the BO loop, as opposed to using SHAP only for post hoc analysis.

Load-bearing premise

The whole comparison assumes the deep neural network surrogate faithfully reproduces the true acoustic absorption of the finite element model across the full design space, because every optimization run and every reported improvement is evaluated on that surrogate rather than on new simulations.

Editorial extensions

If this is right

  • With a 400-iteration budget as the reference, SHAP-informed BO reaches the same objective in under one third of the iterations, which translates into a comparable reduction in expensive simulations or experiments.
  • Repeated SHAP-bound applications yield higher final objectives at equal budget: about 3% for PU80 and 11% for PU90 over standard BO.
  • Because each bound refinement is followed by a rapid improvement and then a plateau, runs can be stopped early; the paper notes the top curve plateaus by iteration 300, saving 25% of the budget without losing the 11% gain.
  • The bound-refinement step is wrapped around the BO loop rather than replacing it, so it can be combined with any Gaussian-process kernel or acquisition function.
  • The method adapts to the material: the sets of influential parameters differ between PU80 and PU90, meaning SHAP-bounding is not applying one fixed reduction but is guided by each material's response surface.

Reading between the lines

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

  • Editorial inference: the same signed-SHAP bound tightening should transfer to other expensive black-box design problems whose objectives show monotonic large-scale trends in at least a few parameters, such as structural or optical design, though the paper only demonstrates it on acoustic coatings.
  • Editorial inference: because the 3% and 11% improvements are computed on the DNN surrogate rather than on new finite element simulations, re-evaluating the final designs in the FEM or in experiments is needed to confirm the physical gain; the paper does not report such validation.
  • Editorial inference: the plateau after each bound refinement suggests an adaptive stopping rule, halt when no bounds change for a fixed number of iterations, which the paper mentions as a possibility for early stopping but does not implement.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes SHAP-Bounded Bayesian Optimization, an algorithm that periodically applies SHAP analysis to a DNN surrogate trained on the data collected so far, then tightens the search bounds of the top-6 features according to whether all SHAP values to one side of the best-so-far point are positive. The method is applied to optimize ten geometric parameters of polyurethane acoustic metamaterial coatings for two materials, PU80 and PU90, using a DNN surrogate instead of finite element simulations to evaluate the objective. The reported results claim that SHAP-informed BO matches a 400-iteration classical BO result in fewer than one third of the iterations and yields final surrogate-objective improvements of about 3% for PU80 and 11% for PU90.

Significance. If the surrogate-level results transfer to the finite element model, the contribution is significant and timely: it is a simple, modular way to inject large-scale trend information into Bayesian optimization, it is clearly specified in Algorithm 1, and the paper provides a helpful toy illustration and repeated-run mean/standard-deviation convergence curves. The authors are also candid about several risk factors, including the possibility that sparse initial sampling may mislead SHAP. The current significance is limited, however, because the headline numbers are computed entirely on the DNN surrogate and because the main baseline is not specified; the paper is a promising algorithmic demonstration but not yet a validated material-design study.

major comments (4)
  1. [Section 2.1; Figs. 3c and 4c] The central quantitative claims ('less than 1/3 of the iterations', '3% improvement', '11% improvement') are computed on the DNN surrogate, not on the finite element model, and Section 2.1 explicitly states that the surrogate is used instead of new FEM simulations. Because Algorithm 1 moves the search into a 10%-margin neighborhood of the best-so-far point in the top-6 features, the refined regions can lie outside the distribution of the initial 100 samples that largely determine the DNN training set; a Pearson correlation of 0.999 does not bound absolute surrogate error there. The paper therefore does not yet demonstrate that the proposed designs improve real acoustic absorption. Please either validate the final designs, and ideally the refined regions, with FEM evaluations, or explicitly restrict all conclusions to surrogate-objective performance and revise the abstract and conclusion accordingly.
  2. [Section 3.3; Figs. 3c and 4c] The comparison method is described only as an 'off-the-shelf domain reduction technique applied after the first 100 random samples.' No name, implementation details, or reference are provided, so the reader cannot tell whether this is a standard baseline or a deliberately weak one. Specify the baseline algorithm precisely, cite a published version if one exists, and describe how its bound updates differ from the proposed SHAP-based updates.
  3. [Algorithm 1; Section 2.3.3] The bound-tightening rule depends on several hand-set quantities: the top-6 feature count, the 10% margin, the requirement that all SHAP values on one side of the best-so-far point be positive, and the update schedule at iterations 100, 150, and 200. No sensitivity analysis is reported for any of these choices, and Section 3.3 itself states that the reliability of SHAP on small datasets is an open question. The robustness claim is therefore not yet supported; at minimum, test the margin and update schedule over a few values and report how often the procedure excludes the best region.
  4. [Section 3.3; Figs. 3c and 4c] The manuscript reports mean curves with confidence intervals but never states the number of independent optimization runs, and the final '3%' and '11%' improvements are quoted without error bars or a statistical comparison. Without this information, the claim that the improvement is 'consistently achieved' cannot be evaluated; add the number of seeds and the dispersion of the final improvements for each method.
minor comments (5)
  1. [Section 3.2] The first sentence says 'Figures 4 (a) and (b) present the results of the SHAP analysis for PU90,' but the section and figure concern PU80; this is a typo.
  2. [Section 3.3] In the paragraph beginning 'The Next curve up,' the text reads 'roughly 6% higher for for PU90 and 2% higher for for PU80'; the duplicated 'for' should be removed.
  3. [Abstract] The phrase 'refine the bounds of the optimization problem automatically automatically' contains a duplicated adverb; the abstract also states 'without increasing the number of simulation iterations,' which is redundant with a same-budget comparison and could be clarified.
  4. [Replication of Results] The statement 'All packages and models that supported this study are publicly available' conflicts with 'Source code may be obtained from the corresponding author upon reasonable request'; please clarify the actual availability policy.
  5. [References] Reference [15] appears to duplicate Reference [11] with different formatting, and Reference [22] has 'n/a' for volume and pages; please check consistency and completeness of all entries.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation; the SHAP-bounded BO gain is an empirical comparison under a shared evaluation oracle, with the reused prior DNN surrogate as external evidence rather than a fitted prediction.

full rationale

The paper's derivation chain is not circular. The objective being optimized is the absorption-weighted sum defined in Eq. 1; the DNN surrogate from prior work [11] supplies the objective's values, and the GPR in BO is trained on those values. SHAP values are computed from the DNN and are used only to move the bounds of later BO queries; they are never inserted back into the objective, and no equation defines the final improvement as a function of the SHAP bounds. The reported comparison, 'less than 1/3 of the iterations' and '3% improvement... 11% improvement,' is made under the same evaluation oracle for the SHAP-bounded and baseline routines, so the acceleration claim is an empirical outcome of an online feedback loop, not a tautology. The main legitimate concern is external validity: all evaluations use the previously published DNN surrogate instead of new FEM runs (Section 2.1), and the authors themselves acknowledge that 'a sparse sampling of the parameter space could yield a misleading SHAP analysis' (Section 3.3). That is a validation gap or correctness risk, not a circular step; the surrogate is prior independent work validated against FEM, and the self-citation does not reduce the present algorithm's output to its own fit.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The method introduces no new physical entities. The key extra assumptions are the fidelity of the surrogate model, the reliability of SHAP explanations on small data, and the heuristic sign-based bound update rule. Several hand-chosen constants (10% margin, top 6 features, update schedule) are not tuned or justified.

free parameters (5)
  • bound_margin_percent = 10
    Algorithm 1 sets new bounds to best value +/- 10%. This hand-chosen margin is not varied or justified.
  • num_top_features = 6
    The algorithm only adjusts bounds for the top 6 SHAP-ranked features. The cutoff is arbitrary and not tested.
  • shap_update_schedule = every 50 iterations from iteration 100
    Section 2.3.3 states SHAP and bound updates happen every 50th iteration starting at 100; the authors note no changes after 250. This schedule is problem-specific.
  • initial_sample_multiplier = 10 * D
    The initial random sample size is 10 times the number of design variables (D=10, so 100 samples). This follows common BO practice but is a selected constant.
  • frequency_weights = w_i = (N+1-i)/N
    The objective weights lower frequencies more heavily. This is a design choice made by the authors, not derived from data.
assumptions (5)
  • domain assumption The DNN surrogate from [11] predicts the true FEM objective with Pearson correlation 0.999 and is used as ground truth for all optimization runs.
    All optimization results and comparisons are computed on this surrogate, so its fidelity is load-bearing.
  • domain assumption SHAP values from the DNN correctly indicate which design parameters improve the objective and in which direction.
    The bound refinement acts on SHAP value signs; if the DNN's explanations are wrong, the refinements could exclude the true optimum.
  • ad hoc to paper The rule 'all SHAP values to the right/left of the best-so-far value are positive' is a reliable way to identify monotonic parameter effects.
    This criterion is introduced in Algorithm 1 and is not derived from any theory. The authors call it 'coarse.'
  • domain assumption The upper and lower bounds in Table 1 correctly encode manufacturing constraints.
    The optimization is constrained to these ranges; if they are wrong, the optimum found is not feasible.
  • ad hoc to paper The unspecified 'off-the-shelf domain reduction' baseline is a representative state-of-the-art comparator.
    The baseline algorithm is not specified in the text, so its fairness cannot be assessed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Interpretable SHAP-bounded Bayesian Optimization for Underwater Acoustic Metamaterial Coating Design." pith.science (2026). https://pith.science/paper/NM7DXQCW

@misc{pith2026250506519,
  author       = {Pith},
  title        = {Pith review of: Interpretable SHAP-bounded Bayesian Optimization for Underwater Acoustic Metamaterial Coating Design},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NM7DXQCW}},
  note         = {Machine review of arXiv:2505.06519}
}
read the original abstract

We developed an interpretability informed Bayesian optimization framework to optimize underwater acoustic coatings based on polyurethane elastomers with embedded metamaterial features. A data driven model was employed to analyze the relationship between acoustic performance, specifically sound absorption and the corresponding design variables. By leveraging SHapley Additive exPlanations (SHAP), a machine learning interpretability tool, we identified the key parameters influencing the objective function and gained insights into how these parameters affect sound absorption. The insights derived from the SHAP analysis were subsequently used to automatically refine the bounds of the optimization problem automatically, enabling a more targeted and efficient exploration of the design space. The proposed approach was applied to two polyurethane materials with distinct hardness levels, resulting in improved optimal solutions compared to those obtained without SHAP-informed guidance. Notably, these enhancements were achieved without increasing the number of simulation iterations. Our findings demonstrate the potential of SHAP to streamline optimization processes by uncovering hidden parameter relationships and guiding the search toward promising regions of the design space. This work underscores the effectiveness of combining interpretability techniques with Bayesian optimization for the efficient and cost-effective design of underwater acoustic metamaterials under strict computational constraints and can be generalized towards other materials and engineering optimization problems.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

34 extracted references · 25 canonical work pages

  1. [11]

    Weeratunge, H.Z., Shireen, I., Sagar, A., Menzel, A.W., Phillips, S., Halgamuge, R., Hajizadeh, E.: A machine learning accelerated inverse design of underwater acoustic polyurethane coatings. Struct. Multidiscipl. Optim.65, 213 (2022) https: //doi.org/10.1007/s00158-022-03322-w

    work page doi:10.1007/s00158-022-03322-w 2022

  2. [15]

    Weeratunge, H., Shireen, Z., Iyer, S., Menzel, A., Phillips, A.W., Halgamuge, S., Sandberg, R., Hajizadeh, E.: A machine learning accelerated inverse design of underwater acoustic polyurethane coatings. Struct. Multidiscipl. Optim.65(8) (2022)

    work page 2022

  3. [18]

    Rheologica Acta62, 477– 490 (2023) https://doi.org/10.1007/s00397-023-01397-w

    Weeratunge, H.D., Robe, A., Menzel, A.W., Phillips, S., Hajizadeh, E.: Bayesian coarsening: rapid tuning of polymer model parameters. Rheologica Acta62, 477– 490 (2023) https://doi.org/10.1007/s00397-023-01397-w

    doi:10.1007/s00397-023-01397-w 2023

  4. [14]

    Rheologica Acta62(10), 477–490 (2023) https://doi.org/10.1007/ s00397-023-01397-w

    Weeratunge, H., Robe, D., Menzel, A., Phillips, A.W., Kirley, M., Smith- Miles, K., Hajizadeh, E.: Bayesian coarsening: rapid tuning of polymer model parameters. Rheologica Acta62(10), 477–490 (2023) https://doi.org/10.1007/ s00397-023-01397-w

    work page 2023

  5. [1]

    In: Guyon, I.e.a

    Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model pre- dictions. In: Guyon, I.e.a. (ed.) Advances in Neural Information Processing Systems, pp. 4765–4774. Curran Associates, Inc., ??? (2017). Available at: https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767- Paper.pdf

    work page 2017

  6. [2]

    Journal of Rheology69(2), 183–199 (2025) https://doi.org/ 10.1122/8.0000930

    Abdolahi, J., Robe, D., Larson, R., Kirley, M., Hajizadeh, E.: Interpretable active learning meta-modeling for the association dynamics of telechelic polymers on colloidal particles. Journal of Rheology69(2), 183–199 (2025) https://doi.org/ 10.1122/8.0000930

    work page doi:10.1122/8.0000930 2025

  7. [3]

    Computatioanl Materials Science256, 113931 (2025) https://doi.org/10.1016/j

    Robe, D., Menzel, A., Phillips, A., Hajizadeh, E.: Smiles to scattering: Automated high-throughput atomistic polyurethane simulations compared with waxs data. Computatioanl Materials Science256, 113931 (2025) https://doi.org/10.1016/j. commatsci.2025.113931

    arXiv 2025

  8. [4]

    Shireen, Z., Hajizadeh, E., Daivis, P., Brandl, C.: Linear viscoelastic shear and bulk relaxation moduli in poly(tetramethylene oxide) (PTMO) using united-atom molecular dynamics. Comput. Mater. Sci.216(111824), 111824 (2023)

    work page 2023

Show all 34 references
  1. [5]

    Wave Motion70, 101–112 (2017) https://doi.org/10.1016/j.wavemoti.2016.10.006

    Sharma, G.S., Skvortsov, A., MacGillivray, I., Kessissoglou, N.: Sound transmis- sion through a periodically voided soft elastic medium submerged in water. Wave Motion70, 101–112 (2017) https://doi.org/10.1016/j.wavemoti.2016.10.006 . Recent Advances on Wave Motion in Fluids a...

    2017 doi

  2. [6]

    The Journal of the Acoustical Society of America138(4), 2537–2547 (2015) https://doi.org/10.1121/1.4931446

    Calvo, D.C., Thangawng, A.L., Layman, C.N., Casalini, R., Othman, S.F.: Under- water sound transmission through arrays of disk cavities in a soft elastic medium. The Journal of the Acoustical Society of America138(4), 2537–2547 (2015) https://doi.org/10.1121/1.4931446

    2015 doi

  3. [7]

    Ranjbar, M., Bayer, M.U.: Design of acoustic coating for underwater stealth in low-frequency ranges. J. Braz. Soc. Mech. Sci. Eng.46, 140 (2024) https://doi. org/10.1007/s40430-024-04720-5

    2024 doi

  4. [8]

    Meng, H., Wen, J., Zhao, H., Wen, X.: Optimization of locally resonant acoustic metamaterials on underwater sound absorption characteristics. J. Sound Vib. 331(20), 4406–4416 (2012) https://doi.org/10.1016/j.jsv.2012.05.027

    2012 doi

  5. [9]

    Sharma, G.S., Skvortsov, A., MacGillivray, I., Kessissoglou, N.: Sound absorption by rubber coatings with periodic voids and hard inclusions. Appl. Acoust.143, 200–210 (2019) https://doi.org/10.1016/j.apacoust.2018.09.003

    2019 doi

  6. [10]

    International Journal of Chemical and Biomolecular Engineering1, 40–44 (2008) 19

    Hajizadeh, E., Garmabi, H.: Response surface based optimization of toughness of hybrid polyamide 6 nanocomposites. International Journal of Chemical and Biomolecular Engineering1, 40–44 (2008) 19

    2008

  7. [12]

    Cai, C., Hung, K.C., Khan, M.S.: Simulation-based analysis of acoustic absorbent lining subject to normal plane wave incidence. J. Sound Vib.291(3), 656–680 (2006)

    2006

  8. [13]

    APL Machine Learning 1(4), 046105 (2023)

    Panisilvam, J., Hajizadeh, E., Weeratunge, H., Bailey, J., Kim, S.: Asymmetric cyclegans for inverse design of photonic metastructures. APL Machine Learning 1(4), 046105 (2023)

    2023

  9. [16]

    npj Computational Materials7(1), 188 (2021) https://doi.org/10.1038/ s41524-021-00656-9

    Liang, Q., Gongora, A.E., Ren, Z., Tiihonen, A., Liu, Z., Sun, S., Deneault, J.R., Bash, D., Mekki-Berrada, F., Khan, S.A., Hippalgaonkar, K., Maruyama, B., Brown, K.A., Fisher III, J., Buonassisi, T.: Benchmarking the perfor- mance of bayesian optimization across multiple exp...

    2021

  10. [17]

    npj Comput

    Shireen, Z., Weeratunge, H., Menzel, A., Hajizadeh, E.: A machine learn- ing enabled hybrid optimization framework for efficient coarse-graining of a model polymer. npj Comput. Mater.8, 224 (2022) https://doi.org/10.1038/ s41524-022-00914-4

    2022

  11. [19]

    Applied Acoustics212, 109603 (2023) https: //doi.org/10.1016/j.apacoust.2023.109603

    Kumar, S., Jin, H., Lim, K.M., Lee, H.P.: Comparative analysis of machine learning algorithms on prediction of the sound absorption coefficient for recon- figurable acoustic meta-absorbers. Applied Acoustics212, 109603 (2023) https: //doi.org/10.1016/j.apacoust.2023.109603

    2023

  12. [20]

    Polymer Testing96, 107115 (2021) https://doi

    Fu, Y., Kabir, I.I., Yeoh, G.H., Peng, Z.: A review on polymer-based materials for underwater sound absorption. Polymer Testing96, 107115 (2021) https://doi. org/10.1016/j.polymertesting.2021.107115 20

    2021

  13. [21]

    Applied Acoustics169, 107476 (2020) https://doi.org/10.1016/j.apacoust.2020.107476

    Jeon, J.H., Yang, S.S., Kang, Y.J.: Estimation of sound absorption coefficient of layered fibrous material using artificial neural networks. Applied Acoustics169, 107476 (2020) https://doi.org/10.1016/j.apacoust.2020.107476

    2020

  14. [22]

    Advanced Intelligent Systemsn/a(n/a), 2400304 https://doi.org/10.1002/aisy.202400304 https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202400304

    Salih, A.M., Raisi-Estabragh, Z., Galazzo, I.B., Radeva, P., Petersen, S.E., Lekadir, K., Menegaz, G.: A perspective on explainable arti- ficial intelligence methods: Shap and lime. Advanced Intelligent Systemsn/a(n/a), 2400304 https://doi.org/10.1002/aisy.202400304 https://on...

    doi

  15. [23]

    In: AIAA SciTech Forumv, National Harbor, United States, p

    Palar, P.S., Zuhal, L.R., Dwianto, Y.B., Shimoyama, K., Morlier, J.: Shapley Additive Explanations for Knowledge Discovery via Surrogate Models. In: AIAA SciTech Forumv, National Harbor, United States, p. 0 (2023). https://doi.org/ 10.2514/6.2023-0332 . https://hal.science/hal...

    2023 doi

  16. [24]

    Journal of Building Pathology and Rehabilitation8, 1–12 (2023) https://doi.org/10.1007/s42107-023-00689-z

    Khalid, S., Song, J., Azad, M.M., Elahi, M.U., Lee, J., Jo, S.H., Kim, H.S.: Pre- diction of compressive strength of high-performance concrete using optimization machine learning approaches with shap analysis. Journal of Building Pathology and Rehabilitation8, 1–12 (2023) http...

    2023 doi

  17. [25]

    Stender, M., Adams, C., Wedler, M., Grebel, A., Hoffmann, N.: Explainable machine learning determines effects on the sound absorption coefficient mea- sured in the impedance tube. J. Acoust. Soc. Am.149(3), 1932–1945 (2021) https://doi.org/10.1121/10.0003755

    2021 doi

  18. [26]

    Chem Sci15(19), 7219–7228 (2024)

    Dalal, R.J., Oviedo, F., Leyden, M.C., Reineke, T.M.: Polymer design via SHAP and bayesian machine learning optimizes pDNA and CRISPR ribonucleoprotein delivery. Chem Sci15(19), 7219–7228 (2024)

    2024

  19. [27]

    Brain Informatics11(1), 10 (2024) https://doi.org/10.1186/ s40708-024-00222-1

    Vimbi, V., Shaffi, N., Mahmud, M.: Interpreting artificial intelligence mod- els: a systematic review on the application of lime and shap in alzheimer’s disease detection. Brain Informatics11(1), 10 (2024) https://doi.org/10.1186/ s40708-024-00222-1

    2024

  20. [28]

    Islam, M.M., Das, P., Rahman, M.M.e.a.: Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with shap analysis. J. Build. Rehabil.9, 94 (2024) https://doi.org/10.1007/ s41024-024-00445-z

    2024

  21. [29]

    Tabatabaei Manesh, M., Nikkhah Dehnavi, A., Tahsildoost, M., Alambeigi, P.: Acoustic design evaluation in educational buildings using artificial intelli- gence. Build. Environ.261, 111695 (2024) https://doi.org/10.1016/j.buildenv. 2024.111695

    2024

  22. [30]

    In: Pro- ceedings of Building Simulation 2021: 17th Conference of IBPSA, pp

    Abarghooie, R., Zomorodian, Z.S., Tahsildoost, M., Shaghaghian, Z.: A machine- learning framework for acoustic design assessment in early design stages. In: Pro- ceedings of Building Simulation 2021: 17th Conference of IBPSA, pp. 1548–1555 21 (2021). https://doi.org/10.26868/2...

    2021

  23. [31]

    Materials Today Communications39, 108804 (2024) https://doi.org/10.1016/j.mtcomm

    Wang, Z., Liu, H., Amin, M.N., Khan, K., Qadir, M.T., Khan, S.A.: Opti- mizing machine learning techniques and shapley additive explanations (shap) analysis for the compressive property of self-compacting concrete. Materials Today Communications39, 108804 (2024) https://doi.or...

    2024

  24. [32]

    In: Proceedings of the 34th International Con- ference on Machine Learning - Volume 70

    Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: Proceedings of the 34th International Con- ference on Machine Learning - Volume 70. ICML’17, pp. 3145–3153. JMLR.org, ??? (2017)

    2017

  25. [33]

    Entropy23(1) (2021) https://doi

    Linardatos, P., Papastefanopoulos, V., Kotsiantis, S.: Explainable ai: A review of machine learning interpretability methods. Entropy23(1) (2021) https://doi. org/10.3390/e23010018

    2021 doi

  26. [34]

    Liu, H., Ong, Y.-S., Cai, J.: A survey of adaptive sampling for global meta- modeling in support of simulation-based complex engineering design. Struct. Multidiscipl. Optim.57(1), 393–416 (2018) 22

    2018

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.