The effective contact area in rough surface contact plays a critical role in
multi-physics phenomena such as wear, sealing, and thermal or electrical
conduction. Although accurate numerical methods, like the Boundary Element
Method (BEM), are available to compute this quantity, their high computational
cost limits their applicability in multi-query contexts, such as uncertainty
quantification, parameter identification, and multi-scale algorithms, where
many repeated evaluations are required. This study proposes a surrogate
modeling framework for predicting the effective contact area using
fast-to-evaluate data-driven techniques. Various machine learning algorithms
are trained on a precomputed dataset, where the inputs are the imposed load and
statistical roughness parameters, and the output is the corresponding effective
contact area. All models undergo hyperparameter optimization to enable fair
comparisons in terms of predictive accuracy and computational efficiency,
evaluated using established quantitative metrics. Among the models, the Kernel
Ridge Regressor demonstrates the best trade-off between accuracy and
efficiency, achieving high predictive accuracy, low prediction time, and
minimal training overhead-making it a strong candidate for general-purpose
surrogate modeling. The Gaussian Process Regressor provides an attractive
alternative when uncertainty quantification is required, although it incurs
additional computational cost due to variance estimation. The generalization
capability of the Kernel Ridge model is validated on an unseen simulation
scenario, confirming its ability to transfer to new configurations. Database
generation constitutes the dominant cost in the surrogate modeling process.
Nevertheless, the approach proves practical and efficient for multi-query
tasks, even when accounting for this initial expense.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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