Academic Journal
Sensitivity Analysis on a Computational Model of Olfaction Using Gaussian Process, Generalized Polynomial Chaos and Neural Networks.
| Title: | Sensitivity Analysis on a Computational Model of Olfaction Using Gaussian Process, Generalized Polynomial Chaos and Neural Networks. |
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| Authors: | Chandrasekaran V; Department of Mathematics, University of North Texas, 1155 Union Circle, Denton, TX, 76203, USA., He Y; Department of Mathematics, and Department of Computer Science and Engineering, University of North Texas, 1155 Union Circle, Denton, TX, 76203, USA. yanyan.he@unt.edu., Ohara D; Department of Mathematics, University of North Texas, 1155 Union Circle, Denton, TX, 76203, USA., Waldrop L; Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA, 92866, USA. |
| Source: | Bulletin of mathematical biology [Bull Math Biol] 2026 Oct 08; Vol. 88 (11). Date of Electronic Publication: 2026 Oct 08. |
| Publication Type: | Journal Article; Research Support, U.S. Gov't, Non-P.H.S. |
| Language: | English |
| Journal Info: | Publisher: Springer Country of Publication: United States NLM ID: 0401404 Publication Model: Electronic Cited Medium: Internet ISSN: 1522-9602 (Electronic) Linking ISSN: 00928240 NLM ISO Abbreviation: Bull Math Biol Subsets: MEDLINE |
| Imprint Name(s): | Publication: New York, NY : Springer Original Publication: New York, Pergamon Press. |
| MeSH Terms: | Smell*/physiology , Models, Biological*, Computer Simulation ; Normal Distribution ; Neural Networks, Computer ; Feedforward Neural Networks ; Nonlinear Dynamics ; Mathematical Concepts ; Hydrodynamics ; Animals ; Humans ; Odorants ; Computational Biology ; Soft Computing |
| Abstract: | The process of odor capture by arrays of chemosensory hairs is a complex phenomenon influenced by multiple factors, such as hair morphology and surrounding fluid characteristics. Understanding how these parameters affect the fluid flow and odor transport is critical for advancing biological understanding and informing the potential design of bio-inspired sensing systems. However, performing comprehensive analyses with high-fidelity computational fluid dynamics models can be computationally expensive. To address this challenge, we investigate three surrogate modeling approaches - Gaussian process regression, generalized polynomial chaos and feedforward neural networks - as efficient numerical approximations to full simulations. These surrogates are trained on a limited number of simulations and then used to predict system responses efficiently. Their predictive accuracy is compared using synthetic examples across a range of scenarios. For the biological application considered here, cross-validation is employed to identify the surrogate model with the best performance. Using these surrogate models, we implement a variance-based global sensitivity analysis method to analyze the impact of uncertainty of inputs (such as morphological variables) on flow characteristics. (© 2026. The Author(s), under exclusive licence to the Society for Mathematical Biology.) |
| Competing Interests: | Declarations. Conflict of interest: The authors have no relevant financial or non-financial interests to disclose. |
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| Grant Information: | W911NF-22-1-0037 Army Research Office |
| Contributed Indexing: | Keywords: Fluid dynamics; Gaussian process; Generalized polynomial chaos; Neural networks; Olfaction; Sensitivity analysis |
| Entry Date(s): | Date Created: 20261008 Date Completed: 20261009 Latest Revision: 20261011 |
| Update Code: | 20261012 |
| DOI: | 10.1007/s11538-026-01768-3 |
| PMID: | 42850391 |
| Database: | MEDLINE |
| ISSN: | 1522-9602 |
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| DOI: | 10.1007/s11538-026-01768-3 |