Physics-informed machine learning

Physics-informed machine learning incorporates known physical laws or constraints, such as governing differential equations, directly into a machine learning model's training process, rather than relying purely on data patterns. This can improve a model's accuracy and generalization, especially when training data is limited, since the model is constrained to respect known physics rather than free to fit any pattern in the data alone.¹

Why physics-informed approaches can need less training data

Physics-informed approaches can perform well with less training data than a purely data-driven model, since physical constraints reduce the space of solutions the model needs to search, which matters in manufacturing contexts where extensive labeled data may not be available.

Where physics-informed machine learning applies

Not on the drawing; this is a modeling technique used within engineering and manufacturing software, not something specified by a part's requirements.

Common mistakes

Assuming physics-informed methods eliminate the need for training data entirely; they still require data, but incorporate physical constraints to make better use of the data available, especially when it is limited.

Does physics-informed machine learning need less data than standard ML?

Often yes, since incorporating known physics reduces the space of possible solutions the model must learn purely from data.

Is this the same as a physics-based simulation?

No, it's a hybrid approach: a machine learning model trained with physical constraints, not a full traditional physics simulation on its own.

Where might this apply in manufacturing?

Potentially in modeling processes governed by known physical relationships, such as heat transfer or material deformation, where pure data-driven models might struggle with limited data.