## 1 Introduction  
State-of-the-art technological information processing happens mainly within the digital realm. Numerical values are quantized, and calculations are performed in discrete-time computational cycles. In contrast, the information carried by the physical world surrounding any technological device is analog and continuous. Twelve years after [PERSON] formalized the notion of digital computing [1], [PERSON] established the theoretical foundation of sampling and interpolation theory [2]. Since then, scientists and engineers have extended [PERSON]'s and [PERSON]'s theories and refined the relevant hardware, making the analog world increasingly accessible to digital information processing. As follows from [PERSON]'s sampling theorem, suitable (infinite) interpolation series uniquely restore any bandlimited continuous-time signal, provided that the signal's energy is finite and the samples are taken at least at the Nyquist rate. In its purest form,this result is present in signal processing and communications technology in the context of analog-digital/digital-analog conversion. In principle, however, any computational discretization method, such as finite-element algorithms, employs a similar paradigm: A set of discrete points, [PERSON] dense for the in-between to become negligible, represents some continuous, physical object (c.f. [3] for a recent example in which the physical \"object\" is an electromagnetic field). Another recent concept in the domain of digital information processing is known as digital twinning. According to the formalization established in [4], digital twinning commonly involves a physical entity to be twinned, a machine-readable description (in some machine-readable language) that represents the entity virtually on an appropriate hardware platform, and an interaction between the entity and its description through measurement and control. In this context, the machine-readable description is the entity's digital twin. More generally, if the particular type of computing hardware is not specified, we refer to the entity's virtual representation as a virtual twin. Furthermore, the term \"entity\" indicates that the virtual twin's physical counterpart does not necessarily have to be an actual object. In theory, any abstract formation--such as, for example, an entire communication network [5], or, as above, an electromagnetic field--qualifies for digital twinning, provided we can characterize it by a suitable mathematical model. Despite the different contexts, digital twinning resembles traditional Shannon Sampling and Interpolation (SSI) in some aspects. Both approaches represent a physical entity (in the context of SSI, a bandlimited signal) using digital data, digitalizing the relevant analog information through a sequential measurement process. However, classical SSI is geared towards completely restoring the physical entity, while digital twinning primarily aims to recover the entity's relevant properties. Within the employed mathematical model, relevant properties usually take the form of a (mathematical) function or relation with a particular interpretation on the practical level, such as the position of a material object at a given time or the total energy contained in an electromagnetic field. Originally associated primarily with Industry 4.0 [6], digital twinning is attracting significant interest in many areas of modern technology. As part of the internet's anticipated evolution towards a unified metaverse, even more facets of the physical environment will connect to virtual space. In particular, information processing will increasingly incorporate the interaction between human multi-modalities (human senses) and the digital domain, c.f. [7]. In order to make human senses experienceable, the computational infrastructure will have to coordinate, process, and distribute the relevant information in real time. This requirement imposes engineers with unprecedented technological challenges regarding optimization, control, and decision-making.