# Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems

arXiv: https://arxiv.org/abs/2504.17354 · HTML: https://mlsanity.com/paper/2504.17354/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 82 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks](https://mlsanity.com/paper/2308.12716/) (2023)
- [Global sensitivity analysis based on Gaussian-process metamodelling for complex biomechanical problems](https://mlsanity.com/paper/2202.01503/) (2022)
- [Bayesian calibration of coupled computational mechanics models under uncertainty based on interface deformation](https://mlsanity.com/paper/2206.05169/) (2022)
- [Solving Bayesian Inverse Problems With Expensive Likelihoods Using Constrained Gaussian Processes and Active Learning](https://mlsanity.com/paper/2312.08085/) (2023)
- [A multi-scale FEM-BEM formulation for contact mechanics between rough surfaces](https://mlsanity.com/paper/1911.10812/) (2019)
- [Algebraic multigrid methods for saddle point systems arising from mortar contact formulations](https://mlsanity.com/paper/1912.09056/) (2019)
- [A framework for the analysis of fully coupled normal and tangential contact problems with complex interfaces](https://mlsanity.com/paper/2110.07932/) (2021)
- [An Efficient Contact Algorithm for Rigid/Deformable Interaction based on the Dual Mortar Method](https://mlsanity.com/paper/2201.01165/) (2022)
- [Scalable computational kernels for mortar finite element methods](https://mlsanity.com/paper/2202.03933/) (2022)
- [Towards a Hybrid Digital Twin: Physics-Informed Neural Networks as Surrogate Model of a Reinforced Concrete Beam](https://mlsanity.com/paper/2405.08406/) (2024)

## Built on by (most cited first, 1 of 1)

- [Graph Neural Network Surrogates for Contacting Deformable Bodies with Necessary and Sufficient Contact Detection](https://mlsanity.com/paper/2507.13459/) (2025)

## Abstract (arXiv)

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.