We survey the mathematical foundations of geometric deep learning, focusing on group equivariant and gauge equivariant neural networks. We develop gauge equivariant convolutional neural networks on arbitrary manifolds $$\mathcal {M}$$M using principal bundles with structure group K and equivariant maps between sections of associated vector bundles. We also discuss group equivariant neural networks for homogeneous spaces $$\mathcal {M}=G/K$$M = G / K , which are instead equivariant with respect to the global symmetry G on $$\mathcal {M}$$M . Group equivariant layers can be interpreted as intertwiners between induced representations of G, and we show their relation to gauge equivariant convolutional layers. We analyze several applications of this formalism, including semantic segmentation and object detection networks. We also discuss the case of spherical networks in great detail, corresponding to the case $$\mathcal {M}=S^2=\textrm{SO}(3)/\textrm{SO}(2)$$M = S 2 = SO ( 3 ) / SO ( 2 ) . Here we emphasize the use of Fourier analysis involving Wigner matrices, spherical harmonics and Clebsch–Gordan coefficients for $$G=\textrm{SO}(3)$$G = SO ( 3 ) , illustrating the power of representation theory for deep learning.

In this paper, we extend the optimization analysis found in the current literature for single-degree-of-freedom vibrational energy harvesters. We numerically derive and analyze the optimization conditions based on unified expressions for piezoelectric and electromagnetic energy harvesters. Our contribution lies in the detailed analysis and comparison of both resonant and anti-resonant states while fully including the effect of intrinsic resistance. We include both the case of excitation by inertial load and prescribed displacement, as the latter has not been elaborated on in the previous literature and provides new insights. We perform a general analysis but also consider typical values of applied piezoelectric and electromagnetic energy harvesters. Our results improve upon previous similar comparative studies by providing new and useful insights regarding optimal load, load power and power input to output efficiency. Our analysis shows an exponential increase in the critical mechanical quality factor due to the resistive loss coefficient. We find that the ratio of mechanical quality factor to resistive loss coefficient, at resonance, increases drastically close to the theoretical maximum for load power. Under the same optimization conditions, an equivalent conclusion can be drawn regarding efficiency. We find that the efficiency at anti-resonance behaves differently and is equal to or larger than the efficiency at resonance. We also show that the optimal load coefficient at resonance has a significant dependence on the mechanical quality factor only when the resistive loss coefficient is large. Our comparison of excitation types supports the previous literature, in a simple and intuitive way, regarding optimal load by impedance matching and power output efficiency. Our modeling and exploration of new parameter spaces provide an improved tool to aid the development of new harvester prototypes.

We consider the relationship between symmetries of two-dimensional autonomous dynamical system in two common formulations; as a set of differential equations for the derivative of each state with respect to time, and a single differential equation in the phase plane representing the dynamics restricted to the state space of the system. Both representations can be analysed with respect to the symmetries of their governing differential equations, and we establish the correspondence between the set of infinitesimal generators of the respective formulations. Our main result is to show that every generator of a symmetry of the autonomous system induces a well-defined vector field generating a symmetry in the phase plane and, conversely, that every symmetry generator in the phase plane can be lifted to a generator of a symmetry of the original autonomous system, which is unique up to constant translations in time. The process of lifting requires the solution of a linear partial differential equation, which we refer to as the lifting condition. We discuss in detail the solution of this equation in general, and exemplify the lift of symmetries in two commonly occurring examples; a mass conserved linear model and a non-linear oscillator model.

Neural ordinary differential equations (NODEs) are geometric deep learning models based on dynamical systems and flows generated by vector fields on manifolds. Despite numerous successful applications, particularly within the flow matching paradigm, all existing NODE models are fundamentally constrained to fixed-dimensional dynamics by the intrinsic nature of the manifold's dimension. In this paper, we extend NODEs to M-polyfolds (spaces that can simultaneously accommodate varying dimensions and a notion of differentiability) and introduce PolyNODEs, the first variable-dimensional flow-based model in geometric deep learning. As an example application, we construct explicit M-polyfolds featuring dimensional bottlenecks and PolyNODE autoencoders based on parametrised vector fields that traverse these bottlenecks. We demonstrate experimentally that our PolyNODE models can be trained to solve reconstruction tasks in these spaces, and that latent representations of the input can be extracted and used to solve downstream classification tasks. The code used in our experiments is publicly available at https://github.com/turbotage/PolyNODE .