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

Vasileios Vatellis

TL;DR

This paper tackles efficient physics data analysis by benchmarking multiple ML approaches, including Physics-Informed Neural Networks (PINNs), on a binary classification task that separates experimentally viable parameter points in a BGL-based model. It demonstrates that XGBoost excels as a fast initial filter, while standard neural networks and PINNs achieve higher accuracy and physics-consistency at the cost of longer training times; the best PINN configuration yields a strong balance between precision and physics adherence. The study introduces a dual-output PINN architecture with a physics-informed loss term $\mathcal{L}_{\text{total}} = \mathcal{L}_{\text{classification}} + \lambda \mathcal{L}_{\text{physics}}$ (with $\lambda = 1$) and shows that incorporating physical constraints can improve interpretability and reliability. Overall, the work highlights the trade-offs between computational efficiency and model sophistication in physics data analysis and outlines concrete directions for generalization, uncertainty quantification, and ensemble approaches.

Abstract

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-including Nearest Neighbors, Decision Trees, Random Forest, 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 Higgs observables and essential parameters. Through this comprehensive analysis, we aim to showcase the 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 and Physics-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.

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

TL;DR

This paper tackles efficient physics data analysis by benchmarking multiple ML approaches, including Physics-Informed Neural Networks (PINNs), on a binary classification task that separates experimentally viable parameter points in a BGL-based model. It demonstrates that XGBoost excels as a fast initial filter, while standard neural networks and PINNs achieve higher accuracy and physics-consistency at the cost of longer training times; the best PINN configuration yields a strong balance between precision and physics adherence. The study introduces a dual-output PINN architecture with a physics-informed loss term (with ) and shows that incorporating physical constraints can improve interpretability and reliability. Overall, the work highlights the trade-offs between computational efficiency and model sophistication in physics data analysis and outlines concrete directions for generalization, uncertainty quantification, and ensemble approaches.

Abstract

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-including Nearest Neighbors, Decision Trees, Random Forest, 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 Higgs observables and essential parameters. Through this comprehensive analysis, we aim to showcase the 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 and Physics-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.

Paper Structure

This paper contains 6 sections, 2 equations, 3 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Dataset Description and Preparation
  3. Methodology
  4. Physics-Informed Neural Networks (PINNs)
  5. Results and Discussion
  6. Future Work

Figures (3)

  • Figure 1: Process Flowchart
  • Figure 2: Comparison of a standard Neural Network Fig \ref{['fig:nn']} and a Physics-Informed Neural Network (PINN) Fig \ref{['fig:pinn']}.
  • Figure 3: Architecture of the Physics-Informed Neural Network (PINN). The network comprises an input layer, followed by multiple fully connected hidden layers (H1 and H2), and a specialized physics-informed layer (highlighted in red), concluding with an output layer. While the illustration shows selective connections for clarity, it is important to note that each layer is fully connected to its subsequent layer. The physics-informed layer (P1, P2, P3) is designed to encode specific physical principles or constraints relevant to the problem domain, facilitating the integration of domain knowledge directly into the learning process.