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The task of modeling and forecasting a dynamical system is one of the oldest problems, and itremains challenging. Broadly, this task has two subtasks: extracting the full dynamical informa-tion from a partial observation, and then explicitly learning the dynamics from this information.We present a mathematical framework in which the dynamical information is represented in theform of an embedding. The framework combines the two subtasks using the language of spaces,maps, and commutations. The framework also unifies two of the most common learning paradigms:delay-coordinates and reservoir computing. We use this framework as a platform for two otherinvestigations of the reconstructed system, its dynamical stability and the growth of error underiterations. We show that these questions are deeply tied to more fundamental properties of theunderlying system, i.e., the behavior of matrix cocycles over the base dynamics, its nonuniformhyperbolic behavior, and its decay of correlations. Thus, our framework bridges the gap betweenuniversally observed behavior of dynamics modeling and the spectral, differential, and ergodic prop-erties intrinsic to the dynamics. 

Abstract Harmonic Hilbert spaces on locally compact abelian groups are reproducing kernel Hilbert spaces (RKHSs) of continuous functions constructed by Fourier transform of weighted$$L^2$$ $L^2$spaces on the dual group. It is known that for suitably chosen subadditive weights, every such space is a Banach algebra with respect to pointwise multiplication of functions. In this paper, we study RKHSs associated with subconvolutive functions on the dual group. Sufficient conditions are established for these spaces to be symmetric Banach$$^*$$ ${}^{\ast }$-algebras with respect to pointwise multiplication and complex conjugation of functions (here referred to as RKHAs). In addition, we study aspects of the spectra and state spaces of RKHAs. Sufficient conditions are established for an RKHA on a compact abelian groupGto have the same spectrum as the$$C^*$$ $C^{\ast }$-algebra of continuous functions onG. We also consider one-parameter families of RKHSs associated with semigroups of self-adjoint Markov operators on$$L^2(G)$$ $L^2\left(G\right)$, and show that in this setting subconvolutivity is a necessary and sufficient condition for these spaces to have RKHA structure. Finally, we establish embedding relationships between RKHAs and a class of Fourier–Wermer algebras that includes spaces of dominating mixed smoothness used in high-dimensional function approximation. 

We investigate the computability (in the sense of computable analysis) of the topological pressure P_top(ϕ) on compact shift spaces X for continuous potentials ϕ:X→R. This question has recently been studied for subshifts of finite type (SFTs) and their factors (sofic shifts). We develop a framework to address the computability of the topological pressure on general shift spaces and apply this framework to coded shifts. In particular, we prove the computability of the topological pressure for all continuous potentials on S-gap shifts, generalised gap shifts, and particular beta-shifts. We also construct shift spaces which, depending on the potential, exhibit computability and non-computability of the topological pressure. We further prove that the generalised pressure function (X,ϕ) ↦P_top(X,ϕ|_X) is not computable for a large set of shift spaces X and potentials ϕ . In particular, the entropy map X↦h_top(X) is computable at a shift spaceXif and only if X has zero topological entropy. Along the way of developing these computability results, we derive several ergodic-theoretical properties of coded shifts which are of independent interest beyond the realm of computability.