Applying PhysicsML When You Do Not Have Large Datasets
Read the full article to learn how PhysicsML is enabling confident, real-time decision-making across engineering sectors.
Published
14 NOV 2025
Est. reading time
2 min
Many organisations either do not collect large volumes of operational data or have only limited historical records. This can make it difficult to use traditional machine learning, which often relies on huge datasets to be effective.
Physics Machine Learning, or PhysicsML, provides a way forward by combining well-established physical laws with whatever data is available.
The physical laws form the backbone of the model, setting clear boundaries that reflect how the real-world system can behave. These laws have been developed, tested and validated over decades, and sometimes centuries, giving engineers a trusted starting point.
The available data is then used to tune the model so it matches the characteristics of the specific asset or process being studied.
This approach effectively augments the dataset, allowing meaningful modelling even when information is sparse. Because the physics-based structure is in place, the model will not produce results that are unrealistic or impossible.
Over time, as more operational data is collected, the model can continue to refine itself, becoming more accurate and more representative of the asset it is monitoring.
In sectors where new assets are being deployed or where legacy systems lack full instrumentation, this method is particularly useful. It enables predictive tools and control logic to be put in place from the beginning, without waiting for years of data collection.
The result is a reliable, adaptive tool that can be used for monitoring, optimisation and forecasting. Industries such as aerospace, energy, defence and manufacturing can gain the benefits of predictive modelling and advanced control strategies without waiting years to build a large dataset.
PhysicsML allows them to make confident, data-driven decisions from day one.
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