The Challenges of Using PhysicsML and Digital Twins
Read the full article to explore how these challenges are being solved across advanced control and modelling applications.
Published
14 NOV 2025
Est. reading time
2 min
While Physics Machine Learning and digital twin technologies offer powerful capabilities, there are challenges that need to be addressed to make them effective.
The first is simplifying a complex system into its core physical equations without losing the accuracy needed for meaningful results. This is particularly difficult when systems operate in variable environments or have a wide range of possible conditions. Choosing the right equations and models requires deep technical understanding of both the asset and the physics that govern it.
Failure modes also need to be considered. These can be hard to capture in a model, especially if the system enters an unexpected state that has not been mapped before. In PhysicsML, tuning is critical. The parameters have to be set correctly for the model to make reliable predictions, and the same applies to control systems in general.
If tuning is poor or incomplete, the model may still function under nominal conditions but produce poor results under stress or change. This limits the value of predictive insights, especially in safety-critical or high-reliability sectors.
Another practical challenge is deciding which parameters to measure. In theory, more data can improve accuracy, but in reality, sensors add cost, weight and complexity. The aim is to select a minimal but robust set of measurements that provide the most useful insights for the model or control strategy.
Finding the right trade-off between fidelity and simplicity is central to building digital twins that are usable, scalable and interpretable. The model must be fast enough to run in real time, but detailed enough to support reliable decision-making.
In both PhysicsML and digital twin applications, these challenges can be addressed by combining strong domain knowledge with advanced optimisation and auto-tuning algorithms. This approach speeds up the development process while keeping models accurate, efficient and reliable.
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