A baby learns by observation how objects move. But without accounting for conservation of momentum, its developed understanding is just an educated guess. Similarly, an artificial neural network (ANN) learns from empirical data how a particular system works, but without explicitly considering the conservation laws that govern that system, it risks making unreliable predictions. To address this limitation, Tom Beucler at the University of California, Irvine, and colleagues have devised a way to hardwire an ANN with such laws. They demonstrated the technique using an atmospheric model for the climate, but they say that their method can be applied to models of any physical system [1].
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