Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks

VASILEIOS VATELLIS — 2025-06-21

In an era increasingly focused on green computing and explainable AI, revisiting traditional approaches in
theoretical and phenomenological particle physics is paramount. This project evaluates various machine
learning (ML)algorithms—includingNearestNeighbors,DecisionTrees, RandomForest,AdaBoost,Naive
Bayes, Quadratic Discriminant Analysis (QDA), and XGBoost—alongside standard neural networks and
a novel Physics-Informed Neural Network (PINN) for physics data analysis. We apply these techniques
to a binary classification task that distinguishes the experimental viability of simulated scenarios based on
Higgsobservablesandessentialparameters.Throughthiscomprehensiveanalysis,weaimtoshowcasethe
capabilities and computational efficiency of each model in binary classification tasks, thereby contributing
to the ongoing discourse on integrating ML and Deep Neural Networks (DNNs) into physics research. In
this study, XGBoost emerged as the preferred choice among the evaluated machine learning algorithms for
its speed and effectiveness, especially in the initial stages of computation with limited datasets. However,
while standard Neural Networks andPhysics-Informed Neural Networks (PINNs) demonstrated superior
performance in terms of accuracy and adherence to physical laws, they require more computational time.
These findings underscore the trade-offs between computational efficiency and model sophistication.

Machine Learning Methods for Sleep Apnoea Detection Based on Imbalanced Pulse and Oximetry Data

Dongjin Yang — 2025-07-02

Sleep apnoea, a disorder impacting both children and adults, typically requires costly and time-intensive diagnostics. This paper introduces a novel framework that uses the wavelet transform to extract features from sleep signals and the RUSBoost algorithm to address the challenge of imbalanced data in detecting sleep apnoea, which enables home self-monitoring. Patient data features short apnoea epochs and long periods of normal breathing, creating imbalances that challenge classification algorithms. The framework was tested on three public datasets with varying imbalance ratios. Significantly, the Childhood Adenotonsillectomy Trial (CHAT) dataset with an ‘apnoea’ to ‘normal’ period ratio of 1:15, effectively reflects actual sleep apnoea signals from children. The proposed framework with the CHAT dataset achieved a maximum accuracy of 91.54%, sensitivity of 72.06%, specificity of 92.39%, and an AUC of 0.923, surpassing state-of-the-art home screening models. For the classification task, this study compared several machine learning techniques, including support vector machine (SVM), K-nearest neighbour (KNN), and Dirichlet process Gaussian mixture model (DPGMM) algorithms. It is found that the RUSBoost algorithm provides the most accurate results when the ratio of the ‘apnoea’ to the ‘normal’ period reaches an imbalance of 1:3 or greater.

Journal of Machine Learning in Fundamental Sciences

Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks

Machine Learning Methods for Sleep Apnoea Detection Based on Imbalanced Pulse and Oximetry Data