The systematic development of coarse-grained (CG) models via the Mori–Zwanzig projector operator formalism requires the explicit description of a deterministic drift term, a dissipative memory term and a random fluctuation term. The memory and fluctuating terms are related by the fluctuation–dissipation relation and are more challenging to sample and describe than the drift term due to complex dependence on space and time. This work proposes a rational basis for a Markovian data-driven approach to approximating the memory and fluctuating terms. We assumed a functional form for the memory kernel and under broad regularity hypothesis, we derived bounds for the error committed in replacing the original term with an approximation obtained by its asymptotic expansions. These error bounds depend on the characteristic time scale of the atomistic model, representing the decay of the autocorrelation function of the fluctuating force; and the characteristic time scale of the CG model, representing the decay of the autocorrelation function of the momenta of the beads. Using appropriate parameters to describe these time scales, we provide a quantitative meaning to the observation that the Markovian approximation improves as they separate. We then proceed to show how the leading-order term of such expansion can be identified with the Markovian approximation usually considered in the CG theory. We also show that, while the error of the approximation involving time can be controlled, the Markovian term usually considered in CG simulations may exhibit significant spatial variation. It follows that assuming a spatially constant memory term is an uncontrolled approximation which should be carefully checked. We complement our analysis with an application to the estimation of the memory in the CG model of a one-dimensional Lennard–Jones chain with different masses and interactions, showing that even for such a simple case, a non-negligible spatial dependence for the memory term exists.

Modern telecommunications are moving towards (massive) multi-input multi-output (MIMO) systems in 5th generation (5G) technology, increasing the dimensionality of the systems dramatically. In this paper, the impairments of radio frequency (RF) power amplifiers (PAs) in a 3 × 3 MIMO system are compensated in both the time and the frequency domains. A three-dimensional (3D) time-domain memory polynomial-type model is proposed as an extension of conventional 2D models. Furthermore, a 3D frequency-domain technique is formulated based on the proposed time-domain model to reduce the dimensionality of the model, while preserving the performance in terms of model errors. In the 3D frequency-domain technique, the bandwidth of the system is split into several narrow sub-bands, and the parameters of the model are estimated for each sub-band. This approach requires less computational complexity, and also the procedure of the parameters estimation for each sub-band can be implemented independently. The device-under-test consists of three RF PAs including input and output cross-talk channels. The proposed techniques are evaluated in both behavioral modeling and digital pre-distortion (DPD) perspectives. The experimental results show that the proposed DPD technique can compensate the errors of non-linearity and memory effects in the both time and frequency domains.

We study origin, parameter optimization, and thermodynamic efficiency of isothermal rocking ratchets based on fractional subdiffusion within a generalized non-Markovian Langevin equation approach. A corresponding multi-dimensional Markovian embedding dynamics is realized using a set of auxiliary Brownian particles elastically coupled to the central Brownian particle (see video on the journal web site). We show that anomalous subdiffusive transport emerges due to an interplay of nonlinear response and viscoelastic effects for fractional Brownian motion in periodic potentials with broken space-inversion symmetry and driven by a time-periodic field. The anomalous transport becomes optimal for a subthreshold driving when the driving period matches a characteristic time scale of interwell transitions. It can also be optimized by varying temperature, amplitude of periodic potential and driving strength. The useful work done against a load shows a parabolic dependence on the load strength. It grows sublinearly with time and the corresponding thermodynamic efficiency decays algebraically in time because the energy supplied by the driving field scales with time linearly. However, it compares well with the efficiency of normal diffusion rocking ratchets on an appreciably long time scale.

Modification of behaviour in response to changes in the environment or ambientconditions, based on memory, is typical of the human and, possibly, many animalspecies.One obvious example of such adaptivity is, for instance, switching to a saferbehaviour when in danger, from either a predator or an infectious disease. In humansociety such switching to safe behaviour is particularly apparent during epidemics.Mathematically, such changes of behaviour in response to changes in the ambient conditionscan be described by models involving switching. In most cases, this switching is assumedto depend on the system state, and thus it disregards the history and, therefore, memory.Memory can be introduced into a mathematical model using a phenomenon known as hysteresis.We illustrate this idea using a simple SIR compartmental model that is applicable inepidemiology. Our goal is to show why and how hysteresis can arise in such a model, andhow it may be applied to describe a variety of memory effects. Our other objective is tointroduce a unified paradigm for mathematical modelling with memory effects inepidemiology and ecology. Our approach treats changing behaviour as an irreversible flowrelated to large ensembles of elementary exchange operations that recently has beensuccessfully applied in a number of other areas, such as terrestrial hydrology, andmacroeconomics. For the purposes of illustrating these ideas in an application to biology,we consider a rather simple case study and develop a model from first principles. Weaccompany the model with extensive numerical simulations which exhibit interestingqualitative effects.

The use of digital predistortion for linearizing a millimeter-wave power amplifier (PA) is investigated. A PA operating at 38 GHz is designed using an accurate non-quasi-static transistor model, taking into account both short- and long-term memory effects. A realistic test signal is then used for the identification of a nonlinear auto-regressive moving average (NARMA) behavioral model of the PA. The NARMA-based digital predistorter is then derived and formulated in terms of basic predistortion cells, especially suitable for efficient implementation in an FPGA. The performance of the predistortion solution is preliminarily assessed by means of computer simulations.

We study the homogenization of parabolic or hyperbolic equations like \[\rho_\varepsilon{\partial^n u_\varepsilon\over \partial t^n}- {\rm div}(a_\varepsilon\nabla u_\varepsilon) =f \ \ \hbox{ in } \quad {\O\times(0,T)}+\ \ \hbox{\rm boundary conditions}, \quad n \in \{1,2\},\] when the coefficients $\rho_\varepsilon$ , $a_\varepsilon$ (defined in Ω) take possibly high values on a ε-periodic set of grain-like inclusions of vanishing measure.Memory effects arise in the limit problem.

We present the semi-conductor Boltzmann equation,which is time-reversible, and indicate that it can be formally derivedby considering the large time and small perturbing potential limitin the Von-Neumann equation (time-reversible). We then rigorously computethe corresponding asymptotics in the case of the Von-Neumann equation onthe Torus. We show that the limiting equation we obtain does not coincidewith the physically realistic model. The former is indeedan equation of Boltzmann type, yet with memory in time, so that it appearsto be time-reversible. We comment on this point, and further describethe properties of the limiting equation.