Data underlying the conference proceeding: Decoupled Hull Resistance Prediction: A Computationally Aware and Physically Plausible Approach for Data-Driven Surrogates

This dataset supports the publication <em>Decoupled Hull Resistance Prediction: A Computationally Aware and Physically Plausible Approach for Data-Driven Surrogates</em>. It contains simulated hydrodynamic resistance data for systematically varied ship hull forms—created using Free-Form Deformation (FFD)—and generated via high-fidelity Computational Fluid Dynamics (CFD) simulations. The objective of the research is to develop and benchmark surrogate models that accurately predict total resistance across different hull geometries and operating conditions, while ensuring physical plausibility and generalization. The dataset includes geometric, hydrostatic, and stability parameters, operating conditions (e.g., Froude number), and the corresponding resistance outputs. It is intended to support studies in surrogate modeling, generalization under data scarcity, and shape–performance relationship analysis in marine hydrodynamics.