Study Introduces Physics-Informed Neural Networks and Uncertainty Quantification for Transformer Condition Monitoring

Global: New Study Explores Physics-Informed Neural Networks for Transformer Monitoring

A paper posted to arXiv on December 20, 2025 by Jose I. Aizpurua examines how integrating physics-based knowledge with machine‑learning techniques can improve the monitoring, diagnostics, and prognostics of electrical transformers. The research, titled “Physics‑Informed Machine Learning for Transformer Condition Monitoring – Part II,” builds on a prior installment and focuses on embedding physical laws and uncertainty quantification into neural‑network models. The work aims to deliver more reliable predictions for critical power assets while addressing data sparsity challenges.

Integrating Physics with Machine Learning

The author outlines the growing trend of coupling domain‑specific physics with data‑driven algorithms, arguing that such hybrid models can capture underlying physical behavior that pure data‑centric approaches may miss. By incorporating governing equations directly into the loss functions of neural networks, the study seeks to enhance model interpretability and reduce the risk of overfitting to limited transformer sensor data.

Advancements in Physics‑Informed Neural Networks

Central to the paper is a review of Physics‑Informed Neural Networks (PINNs), which are applied to spatiotemporal thermal modeling and solid‑insulation ageing in transformers. The author demonstrates how PINNs can solve partial differential equations that describe heat diffusion and degradation processes, providing a unified framework for both forward simulation and inverse parameter estimation.

Quantifying Uncertainty with Bayesian PINNs

To address epistemic uncertainty, the study introduces Bayesian PINNs, a probabilistic extension that treats network weights as random variables. This approach enables the generation of predictive intervals, offering stakeholders a quantitative measure of confidence when operating under sparse measurement conditions. The Bayesian formulation is presented as a principled way to assess model robustness.

Application to Transformer Thermal Modeling

Using a case study of a high‑voltage power transformer, the paper illustrates how the combined PINN and Bayesian framework can predict temperature distributions across winding and core components. Results indicate that the physics‑aware model aligns closely with benchmark finite‑element simulations while requiring far fewer computational resources.

Future Research Directions

The author concludes by highlighting several emerging avenues, including multi‑physics extensions that incorporate mechanical stress, real‑time deployment on edge devices, and integration with existing asset‑management platforms. These suggestions point toward a broader vision of trustworthy, physics‑aware artificial intelligence for critical infrastructure.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.