TY  - CONF
A1  - Zinas, Orestis
T1  - A Modular Gaussian Process Regression Toolbox for Uncertainty Aware Geotechnical Site Characterization
N2  - A modular Gaussian Process Regression toolbox for efficient large-scale geotechnical site characterization from sparse 1D data was presented at the Third Future of Machine Learning in Geotechnics (3FOMLIG), Florence, Italy, October 16, 2025. The PyTorch/GPyTorch-based framework enables multivariate modeling of correlated soil properties and joint regression-classification of continuous CPT parameters with categorical soil units through Dirichlet transformations. Stochastic Variational Inference reduces computational complexity from O(N³) to O(M³), enabling GPU-accelerated processing of 100,000+ measurements. Validated on a 33 km² North Sea offshore wind farm site with 100+ sparse investigation points, the toolbox generates uncertainty-aware 3D predictions, supporting univariate, multivariate (LMC), and sequential multi-group modeling strategies.
T2  - Third Future of Machine Learning in Geotechnics (3FOMLIG)
CY  - Florence, Italy
DA  - 15.10.2025
KW  - Probabilistic site-characterization
KW  - Gaussian process regression
KW  - Bayesian inference
KW  - Offshore Wind Farms
PY  - 2025
AN  - OPUS4-64423
LA  - eng
AD  - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER  -