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This chapter offers an in-depth discussion of various nanoelectronic and nanoionic synapses along with the operational mechanisms, capabilities and limitations, and directions for further advancements in this field. We begin with overarching mechanisms to design artificial synapses and learning characteristics for neuromorphic computing. Silicon-based synapses using digital CMOS platforms are described followed by emerging device technologies. Filamentary synapses that utilize nanoscale conducting pathways for forming and breaking current shunting routes within two-terminal devices are then discussed. This is followed by ferroelectric devices wherein polarization states of a switchable ferroelectric layer are responsible for synaptic plasticity and memory. Insulator–metal transition-based synapses are described wherein a sharp change in conductance of a layer due to external stimulus offers a route for compact synapse design. Organic materials, 2D van der Waals, and layered semiconductors are discussed. Ionic liquids and solid gate dielectrics for multistate memory and learning are presented. Photonic and spintronic synapses are then discussed in detail.
The classical problem of steady rarefied gas flow past an infinitely thin circular disk is revisited, with particular emphasis on the gas behaviour near the disk edge. The uniform flow is assumed to be perpendicular to the disk surface. An integral equation for the velocity distribution function, derived from the linearised Bhatnagar–Gross–Krook model of the Boltzmann equation and subject to diffuse reflection boundary conditions, is solved numerically. The numerical method fully accounts for the discontinuity in the velocity distribution function that arises due to the presence of the edge. It is found that a kinetic boundary layer forms near the disk edge, extending over several mean free paths, and that its magnitude scales as $\textit{Kn}^{1/2}$ as the Knudsen number $\textit{Kn}$ (defined with respect to the disk radius) tends to zero. A thermal polarisation effect, previously studied for spherical geometries, is also observed in the disk case, with a more pronounced manifestation near the edge that exhibits the same $\textit{Kn}^{1/2}$ scaling. The drag force acting on the disk is computed over a wide range of Knudsen numbers and shows good agreement with existing results for a hard-sphere gas and in the near-free-molecular regime.
In Navier–Stokes (NS) turbulence, large-scale turbulent flows inevitably determine small-scale flows. Previous studies using data assimilation with the three-dimensional (3-D) NS equations indicate that employing observational data resolved down to a specific length scale, $\ell ^{\rm 3\text{-}D}_{\ast }$, enables the successful reconstruction of small-scale flows. Such a length scale of ‘essential resolution of observation’ for reconstruction $\ell ^{\rm 3\text{-}D}_{\ast }$ is close to the dissipation scale in three-dimensional NS turbulence. Here, we study the equivalent length scale in two-dimensional (2-D) NS turbulence, $\ell ^{\rm 2\text{-}D}_{\ast }$, and compare with the three-dimensional case. Our numerical studies using data assimilation and conditional Lyapunov exponents reveal that, for Kolmogorov flows with Ekman drag, the length scale $\ell ^{\rm 2\text{-}D}_{\ast }$ is actually close to the forcing scale, substantially larger than the dissipation scale. Furthermore, we discuss the origin of the significant relative difference between the length scales, $\ell ^{\rm 2\text{-}D}_{\ast }$ and $\ell ^{\rm 3\text{-}D}_{\ast }$, based on inter-scale interactions, ‘cascades’ and orbital instabilities in turbulence dynamics.
We introduce a description of passive scalar transport based on a (deterministic and hyperbolic) Liouville master equation. Defining a noise term based on time-independent random coefficients, instead of time-dependent stochastic processes, we circumvent the use of stochastic calculus to capture the one-point space–time statistics of solute particles in Lagrangian form deterministically. To find the proper noise term, we solve a closure problem for the first two moments locally in a streamline coordinate system, such that averaging the Liouville equation over the coefficients leads to the Fokker–Planck equation of solute particle locations. This description can be used to trace solute plumes of arbitrary shape, for any Péclet number, and in arbitrarily defined grids, thanks to the time reversibility of hyperbolic systems. In addition to grid flexibility, this approach offers some computational advantages as compared with particle tracking algorithms and grid-based partial differential equation solvers, including reduced computational cost, no Monte-Carlo-type sampling and unconditional stability. We reproduce known analytical results for the case of simple shear flow and extend the description of mixing in a vortex model to consider diffusion radially and nonlinearities in the flow, which govern the long time decay of the maximum concentration. Finally, we validate our formulation by comparing it with Monte Carlo particle tracking simulations in a heterogeneous flow field at the Darcy (continuum) scale.
Dense granular flows exhibit both surface deformation and secondary flows due to the presence of normal stress differences. Yet, a complete mathematical modelling of these two features is still lacking. This paper focuses on a steady shallow dense flow down an inclined channel of arbitrary cross-section, for which asymptotic solutions are derived by using an expansion based on the flow’s spanwise shallowness combined with a second-order granular rheology. The leading-order flow is uniaxial with a constant inertial number fixed by the inclination angle. The streamwise velocity then corresponds to a lateral juxtaposition of Bagnold profiles scaled by the varying flow depth. The correction at first order introduces two counter-rotating vortices in the plane perpendicular to the main flow direction (with downwelling in the centre), and an upward curve of the free surface. These solutions are compared with discrete element method simulations, which they match quantitatively. This result is then used together with laboratory experiments to infer measurements of the second-normal stress difference in dense dry granular flow.
Accurate estimation of dark matter halo masses for galaxy groups is central to studies of galaxy evolution and for leveraging group catalogues as cosmological probes. In this work, we present a comprehensive evaluation and calibration of two complementary halo mass estimators: a dynamical estimator based on a modified virial theorem (MVT) and an empirical summed stellar mass to halo mass relation (sSHMR), which uses the summed mass of the three most massive group galaxies as a proxy for halo mass. Using a suite of state-of-the-art semi-analytic models (SAMs; Shark, SAGE, and GAEA) to produce observationally motivated mock light-cone catalogues, we rigorously quantify the accuracy, uncertainty, and model dependence of each method. The MVT halo mass estimator achieves negligible systematic bias (mean $\Delta = -0.01$ dex) and low scatter (mean $\sigma = 0.20$ dex) as a function of the predicted halo mass, with no sensitivity to the SAM baryonic physics. The calibrated sSHMR yields the highest precision, with mean $\Delta = 0.02$ dex and mean $\sigma = 0.14$ dex as a function of the predicted halo mass but exhibits greater model dependence due to its sensitivity to varying baryonic physics and physical prescriptions across the SAMs. We demonstrate the application of these estimators to observational group catalogues, including the construction of the empirical halo mass function and the mapping of quenched fractions in the stellar mass–halo mass plane. We provide clear guidance on the optimal application of each method: the MVT is recommended for GAMA-like surveys ($i \lt 19.2$) calibrated to $z \lt 0.1$ and should be used for studies that require minimal model dependence, while the sSHMR is optimal for high-precision halo mass estimation across diverse catalogues with magnitude limits of $Z \lt 21.2$ or brighter and to redshifts of $z \leq 0.3$. These calibrated estimators will be of particular value for upcoming wide-area spectroscopic surveys, enabling robust and precise analyses between the galaxy–halo connection and the underlying dark matter distribution.
The stability of free jets is one of the fundamental problems that has driven the development of new theoretical and numerical methods in fluid mechanics. Extensive research has focused on the convective instabilities that characterise their elusive dynamics. However, in real-world configurations, free jets are often confined by solid walls which may exhibit different degrees of flexibility. The present paper presents, for the first time, evidence that even slightly flexible nozzles can lead to global instabilities. To show it, we adopted the classical tools of linear stability analysis, solving the fluid–structure interaction (FSI) problem by an arbitrary Lagrangian–Eulerian method, formulating a monolithic three-field problem. The investigation of the base flow properties reveals the effect of the Reynolds number, based on the bulk velocity and channel height, in the range $[50,200]$ and of the plate stiffness on the nozzle deformation and on the jet flow development. Exploiting an idea first proposed by Luchini and Charru, we develop an ad hoc quasi-one-dimensional model capable of predicting the displacement of elastic boundaries even for large displacements. The stability and sensitivity analysis shows that the interaction of the flow with the flexible structure leads to two categories of globally unstable modes: sinuous (in-phase) modes and varicose (out-of-phase) modes. All the results presented have been cross-checked with direct numerical simulations of the nonlinear FSI system, revealing that the instabilities correspond to supercritical bifurcations. This work has significant implications for many natural and industrial phenomena where a jet is produced by a compliant nozzle.
The chapter introduces key codesign principles across multiple layers of the design stack highlighting the need for cross-layer optimizations. Mitigation of various non-idealities stemming from emerging devices such as device-to-device variations, cycle-to-cycle variations, conductance drift, and stuck-at-faults through algorithm–hardware codesign are discussed. Further, inspiration from the brain’s self-repair mechanism is utilized to design neuromorphic systems capable of autonomous self-repair. Finally, an end-to-end codesign approach is outlined by exploring synergies of event-driven hardware and algorithms with event-driven sensors, thereby leveraging maximal benefits of brain-inspired computing.
This chapter provides a selection of problems relevant to the field of neuromorphic computing that intersects materials science, electrical engineering, computer science, neural networks, and device design for realizing AI in hardware and algorithms. The emphasis on interdisciplinary nature of neuromorphic computing is apparent.
This chapter starts with a discussion on models informing probability versus the case where probability is inherent in the model. The chapter also goes into detail to argue why a particular interpretation of quantum mechanics, Bohmian economics, can be useful in finance. We provide for an example of how such mechanics can be applied to daily returns on commodity prices. We also briefly look into the potential connection between Bohmian mechanics and a macroscopic fluid system.
The chapter discusses concepts in plasticity that go beyond memory. Several examples are discussed starting with the complexity of dendritic structure in biological neurons, nonlinear summation of signals from synapses by neurons and the vast range of plasticity that has been discovered in biological brain circuits. Learning and memory are commonly assigned to synapses; however, non-synaptic changes are important to consider for neuromorphic hardware and algorithms. The distinction between bioinspired and bio-realistic designs of hardware for AI is discussed. While synaptic connections can undergo both functional and structural plasticity, emulating such concepts in neuromorphic computing will require adaptive algorithms and semiconductors that can be dynamically reprogrammed. The necessity for close collaboration between neuroscience and neuromorphic engineering community is highlighted. Methods to implement lifelong learning in algorithms and hardware are discussed. Gaps in the field and directions for future research and development are discussed. The prospects for energy-efficient neuromorphic computing with disruptive brain-inspired algorithms and emerging semiconductors are discussed.
The chapter begins with physics and mathematical description of the nonlinear dynamics seen in biological neurons and their adaptation into neuromorphic hardware. Various abstractions of the Hodgkin–Huxley model of squid neuron have been studied in neuromorphic computing. Filamentary threshold switches that can act as neurons are discussed. The combination of ionic and electronic relaxation pathways offers unique abilities to design low-power artificial neurons. Ferroelectric, insulator–metal transition, 2D materials, and organic semiconductor-based neurons are discussed wherein modulation of long-range transport and/or bound charge displacement are utilized for neuron function. Besides electron transport, light and spin state can also be effectively utilized to create photonic and spintronic neurons respectively. The chapter should provide the reader a comprehensive insight into design of artificial neurons that can generate action potentials, spanning various classes of inorganic and organic semiconductors, and different stimuli for input and readout of signals such as voltage, light, spin current, and ionic currents.