There has been enormous progress in the last few years in designing neural
    networks that respect the fundamental symmetries and coordinate freedoms of
    physical law. Some of these frameworks make use of irreducible representations,
    some make use of high-order tensor objects, and some apply symmetry-enforcing
    constraints. Different physical laws obey different combinations of fundamental
    symmetries, but a large fraction (possibly all) of classical physics is
    equivariant to translation, rotation, reflection (parity), boost (relativity),
    and permutations. Here we show that it is simple to parameterize universally
    approximating polynomial functions that are equivariant under these symmetries,
    or under the Euclidean, Lorentz, and Poincar\’e groups, at any dimensionality
    $d$. The key observation is that nonlinear O($d$)-equivariant (and
    related-group-equivariant) functions can be universally expressed in terms of a
    lightweight collection of scalars — scalar products and scalar contractions of
    the scalar, vector, and tensor inputs. We complement our theory with numerical
    examples that show that the scalar-based method is simple, efficient, and

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