Physics-Informed Machine Learning: Insight with Engineering Rigour
Physics-Informed Machine Learning means models do not just recognise patterns, they understand the systems they represent.
Published
13 NOV 2025
Est. reading time
1 min
Physics Machine Learning (PhysicsML) brings a new level of precision to engineering analysis. Unlike traditional machine learning, which identifies patterns in large datasets, PhysicsML integrates those patterns with known physics and system constraints.
This fusion creates models that are not only predictive but also explainable. Engineers can trust the output because it aligns with their understanding of the system. It respects thermodynamic laws, material behaviour, and system dynamics.
For example, in battery design for electric vehicles or marine platforms, PhysicsML allows engineers to model load cases, charging cycles, and temperature effects. The outcome is a more accurate estimate of required capacity. That enables right-sizing the battery, minimising weight, cost, and overengineering.
Right-sizing has a cascade effect. A lighter battery reduces vehicle mass, which improves efficiency, range, and handling. This creates a cycle of optimisation that enhances the entire system.
PhysicsML is especially powerful in sparse data environments. When high-frequency telemetry is unavailable or sensors are limited, the physics-based nature of the model enables it to infer likely behaviours based on related signals.
At Williams Grand Prix Technologies, we use PhysicsML to enhance digital twins, optimise control systems, and accelerate simulation workflows. It is a foundational tool in our approach to engineering optimisation across all sectors.
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