Comparison of input selection methods for neural networks applied to complex fluid dynamic inverse problem

Efficient identification of informative inputs is critical when training Machine Learning (ML) surrogates on large, multi-sensor datasets. In this paper, we benchmark several input selection methods from the literature alongside new methods proposed here. A baseline method based on expert-driven (human) selection is used as a reference. All methods are evaluated on a challenging inverse problem, in which Computational Fluid Dynamic (CFD) simulations are used to train a Deep Neural Network (DNN) to infer unknown momentum source terms from discrete velocity measurements. The proposed methodology does not explicitly depend on the geometry of the domain and is therefore transferable to other problems involving sparse sensor measurements, although domain-specific validation may still be required. The results show that four input selection methods reduce the number of inputs to as few as five, with minimal impact on the mean average predictive error. This corresponds to a forty-fold reduction relative to the original number of inputs. Analysis of the top four inputs shows that each method selects different locations, indicating that multiple combinations can yield similar accurate results. The top four methods significantly outperform the baseline method based on human selection. This study demonstrates that input selection methods reduce computational costs during both training and inference stages. They also lower experimental demands by identifying high-value sensor locations, thereby reducing the number of required sampling points. These findings suggest that input selection methods should be considered standard practice in ML applications with complex scenarios constrained by limited experimental data. • Identifying key input data is critical in sensor-based Machine Learning problems. • Input selection methods were tested on challenging inverse problem in Fluid Mechanics. • Unsupervised methods outperformed baseline expert-based (human) input selection. • Three methods reduced original inputs by a factor of 40 with minimal inference error. • New methods proposed in this study proved competitive against more complex approaches.