Using Physics Machine Learning When Data Is Limited
Read the full article to see how PhysicsML is unlocking new possibilities in alternative industries.
Published
14 NOV 2025
Est. reading time
2 min
In many industries, there is not an abundance of high-quality data to work with. This can make it difficult to develop accurate models using traditional machine learning methods alone. Physics Machine Learning, or PhysicsML, addresses this challenge by combining physics-based models with data-driven techniques.
The physics-based model provides the structure, defining the boundaries of what is physically possible. Data-driven tools, such as neural networks, are then used to tune the model so it matches the available dataset. This creates a hybrid approach that can produce reliable results even when data is sparse.
This approach is particularly useful for assets that are difficult to instrument, systems that are costly to test, or operations where downtime is limited. The model benefits from known equations and governing behaviours, which guide learning and reduce the risk of overfitting or invalid predictions.
Once built, the model can be used for applications such as predictive maintenance or performance optimisation. It can also incorporate state estimators or regression tools to draw insights from the system and even forecast future behaviour. This enables engineers to make informed decisions about when to carry out maintenance, how to improve efficiency or how to prepare for changes in operating conditions.
By grounding the model in the laws of physics, PhysicsML avoids producing results that are unrealistic or impossible. As the system gathers more data over time, the model continues to adapt and improve, providing a better representation of the real-world asset it is monitoring.
Because the physical structure is preserved, outputs remain interpretable to engineers and system operators. This is particularly important in safety-critical and regulated environments, where trust in the model is essential.
This method is just as applicable to aerospace, energy and manufacturing as it is in motorsport, making it a powerful way to deliver insight and optimisation where traditional approaches fall short.