A conventional network learns the correlations present in its training set and nothing else. A physics-informed neural network adds a second term to the loss function: a measure of how far each prediction is from satisfying an equation known to hold, such as an energy balance, Arrhenius kinetics or a heat transfer relation. The equation constrains the whole solution space instead of acting as one more input column.
Why it matters for industrial assets
Industrial datasets are unbalanced by construction. Failures are rare, sensors cover a narrow band of operating conditions, and the interesting cases are the ones that have not happened yet. A purely statistical model interpolates well inside that band and degrades without warning outside it. The physics term keeps extrapolation inside what the equations allow, and it makes the output auditable: a prediction can be traced back to a mechanism instead of to an unexplained weight.
Where BeAI uses it
CorrosionAI is a physics-informed graph neural network: the graph carries the topology of the pipe network and the physics layer embeds electrochemical and kinetic equations, among them Arrhenius, Nernst and Marcus theory. InverterAI applies the same idea to power electronics, with Coffin-Manson thermal fatigue, Arrhenius ageing and Foster or Cauer thermal networks constraining the remaining useful life estimate.
What it does not solve
A PINN needs the governing physics to be known and expressible. Where the mechanism is unknown, or where the outcome is dominated by human decisions and logistics, the constraint adds cost and no accuracy. Training is heavier than for an equivalent statistical model, and the project needs a domain engineer next to the data scientist, not instead of.