Predicting Newtonian cooling with machine learning: a comparative analysis of gradient boosting and random forest models

Eko Sulistya

Abstract

This study investigates the use of artificial intelligence, specifically machine learning models, to predict temperature reduction in Newtonian cooling experiments involving varying volumes of water. Two regression models, Gradient Boosting Regression and Random Forest Regressor, were utilized to learn from empirical data. The findings indicate that both models are capable of accurately predicting cooling behavior, with the Random Forest model demonstrating superior accuracy for the dataset used. The machine learning models effectively represent the theoretical model of Newton’s Law of Cooling, which is characterized by an exponential decay curve. Furthermore, the cooling constant for each volume was estimated using curve fitting techniques. This research underscores the potential of AI in modeling complex physical processes, particularly in real-world scenarios where the relationships between physical variables are intricate and challenging to express analytically. With sufficient data, AI can adeptly predict variable changes based on fluctuations in others. As technology continues to advance, AI is poised to assume an increasingly critical role in experimental and industrial applications involving complex physical systems. The novelty of this study lies in its comparative analysis to identify the optimal machine learning model—Gradient Boosting Regression or Random Forest Regressor—for accurately predicting Newtonian cooling behavior. Additionally, this research introduces an automated data acquisition approach using a datalogger, significantly enhancing precision and practicality compared to traditional manual methods involving a stopwatch and thermometer.

Keywords

Newtonian Cooling; Machine Learning; Temperature Prediction; Random Forest; Gradient Boosting

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References

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DOI: https://doi.org/10.20961/jphystheor-appl.v9i2.105932

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