[2603.21033] TabPFN Extensions for Interpretable Geotechnical Modelling

Computer Science > Computational Engineering, Finance, and Science arXiv:2603.21033 (cs) [Submitted on 22 Mar 2026] Title:TabPFN Extensions for Interpretable Geotechnical Modelling Authors:Taiga Saito, Yu Otake, Daijiro Mizutani, Stephen Wu View a PDF of the paper titled TabPFN Extensions for Interpretable Geotechnical Modelling, by Taiga Saito and 3 other authors View PDF HTML (experimental) Abstract:Geotechnical site characterisation relies on sparse, heterogeneous borehole data where uncertainty quantification and model interpretability are as critical as predictive accuracy for reliable engineering decisions. This paper presents an exploratory investigation into the use of TabPFN, a transformer-based tabular foundation model using in-context learning, and its extension library tabpfn-extensions for two geotechnical inference tasks: (1) soil-type classification using N-value and shear-wave velocity data from a synthetic geotechnical dataset, and (2) iterative imputation of five missing mechanical parameters ($s_\mathrm{u}$, $E_{\mathrm{u}}$, ${\sigma'}_\mathrm{p}$, $C_\mathrm{c}$, $C_\mathrm{v}$) in benchmark problem BM/AirportSoilProperties/2/2025. We apply cosine-similarity analysis to TabPFN-derived embeddings, visualise full posterior distributions from an iterative inference procedure, and compute SHAP-based feature importance, all without model retraining. Learned embeddings clearly separate Clay and Sand samples without explicit soil-type supervision; iterative i...