  Conservation laws are of great theoretical and practical interest. We
describe a novel approach to machine learning conservation laws of
finite-dimensional dynamical systems using trajectory data. It is the first
such approach based on kernel methods instead of neural networks which leads to
lower computational costs and requires a lower amount of training data. We
propose the use of an "indeterminate" form of kernel ridge regression where the
labels still have to be found by additional conditions. We use here a simple
approach minimising the length of the coefficient vector to discover a single
conservation law.
