64423 2025 eng lecture 0 -- -- -- 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. Third Future of Machine Learning in Geotechnics (3FOMLIG) Florence, Italy 15.10.2025 17.10.2025 0 publish false true Orestis Zinas eng uncontrolled Probabilistic site-characterization eng uncontrolled Gaussian process regression eng uncontrolled Bayesian inference eng uncontrolled Offshore Wind Farms Angewandte Physik 7 Bauwerkssicherheit 7.2 Ingenieurbau Energie Datei im Netzwerk der BAM verfügbar ("Closed Access") Präsentation Windenergie