Learning in complicated systems where we know that there is a conservation law in effect. Or, more advanced, learning a conservation law that we did not know was in effect. As seen in especially ML for physics. This is not AFAIK a particular challenge in traditional parametric statistics where we can usually impose conservation laws on a problem through the likelihood, but in nonparametric models, or models with overparameterisation such as neural nets this can get fiddly. Where does conservation of mass, momentum, energy etc reside in a convnet? Or what if we do not need to conserve a quantity exactly but which to regularise the system towards a conservation law?

POssibly also in this category:
There is a particular type of statistical-mechanical law on the learning process itself, specifically, energy-conservation in neural net signal propagation, which is not a conservation law in the regression model *per se*, but stability guarantee on the model and its traiing process.
This is the deep learning as dynamical system trick.

Without trying to impose constraints on the model, there are a whole bunch of conservation laws and symmetries exploited in *training procedures*, for example in the potential theory, in the statistical mechanics of learning, in the use of conservation laws in Hamiltonian Monte Carlo.

## Incoming

I wonder if the Learning invariant representations idea could help.

Recent entrants in this area:

we demonstrate principled advantages of enforcing conservation laws of the form gφ(fθ(x))=gφ(x) by considering a special case where preimages under gφ form affine subspaces.

Noether networks look interesting.

Also related, learning with a PDE constraint.

Not sure where Dax et al. (2021) fits in.

Erik Bekkers’ seminar on Group Equivariant Deep Learning looks interesting.

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