# Correlation between morphological evolution of splashing drop and exerted impact force revealed by interpretation of explainable artificial intelligence

arXiv: https://arxiv.org/abs/2309.10266 · HTML: https://mlsanity.com/paper/2309.10266/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 68 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Machine Learning for Fluid Mechanics](https://mlsanity.com/paper/1905.11075/) (2019)
- [Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning](https://mlsanity.com/paper/2008.10740/) (2020)
- [Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning](https://mlsanity.com/paper/1907.06011/) (2019)
- [TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition](https://mlsanity.com/paper/1703.10667/) (2017)
- [Image features of a splashing drop on a solid surface extracted using a feedforward neural network](https://mlsanity.com/paper/2201.09541/) (2022)
- [Prediction of the morphological evolution of a splashing drop using an encoder-decoder](https://mlsanity.com/paper/2303.14109/) (2023)

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

- [Predicting Energy Budgets in Droplet Dynamics: A Recurrent Neural Network Approach](https://mlsanity.com/paper/2403.16144/) (2024)

## Abstract (arXiv)

This study reveals a possible correlation between splashing morphology and the normalized impact force exerted by an impacting drop on a solid surface. This finding is obtained from a newly proposed feature extraction method and a subsequent interpretation of the classification of splashing and non-splashing drops performed by an explainable artificial intelligence (XAI) video classifier. Notably, the values of the weight matrix elements of the XAI that correspond to the extracted features are found to change with the temporal evolution of the drop morphology. We compute the rate of change of the contributions of each frame with respect to the classification value of a video as an important index to quantify the contributions of the extracted splashing and non-splashing features at different impact times to the classification of the XAI model. Remarkably, the rate computed for the extracted splashing features is found to closely match the profile of the normalized impact force, where the splashing features are most pronounced immediately after the normalized impact force reaches its peak value. This study has provided an example that clarifies the relationship between the complex morphological evolution of a splashing drop and physical parameters by interpreting the classification of an XAI video classifier.