Efficient Simulation Algorithm Research Articles

Light-induced ultrafast spin transport in multilayer metallic films originates from sp - d spin exchange coupling

Ultrafast interaction between the femtosecond laser pulse and the magnetic metal provides an efficient way to manipulate the magnetic states of matter. Numerous experimental advancements have been made on multilayer metallic films in the last two decades. However, the underlying physics remains unclear. Here, relying on an efficient ab initio spin dynamics simulation algorithm, we revealed the physics that can unify the progress in different experiments. We found that light-induced ultrafast spin transport in multilayer metallic films originates from the sp - d spin-exchange interaction, which can induce an ultrafast, large, and pure spin current from ferromagnetic metal to nonmagnetic metal without charge carrier transport. The resulting trends of spin demagnetization and spin flow are consistent with most experiments. It can explain a variety of ultrafast light-spin manipulation experiments with different systems and different pump-probe technologies, covering a wide range of work in this field.

Efficient algorithm for full-state quantum circuit simulation with DD compression while maintaining accuracy

Efficient algorithm for full-state quantum circuit simulation with DD compression while maintaining accuracy

An efficient topology partitioning algorithm for system-level parallel simulation of mega satellite constellation communication networks

Satellite Internet, as an important component of the integrated space-ground information network, is a hot research hotspot nowadays. Many scholars have undertaken research in the areas of constellation networking design, network protocol design, and communication performance assessment, and their main research tool is software simulation. Traditional stand-alone network simulation simulators based on OPNET or NS3 are constrained in the simulation efficiency of mega satellite networks because of the limitations of computer hardware conditions and software performance. Based on the above characteristics, we propose a parallel network simulation architecture based on low correlation between different areas of the global satellite network, and in order to improve the parallel network simulation performance, the network topology needs to be divided effectively. Therefore, firstly we consider CPU and memory resource consumption as a measure of topology partitioning performance indicators, propose a resource assessment algorithm and use the result of this assessment as the topology partitioning optimization objective; secondly, we propose a load balancing based intelligent topology partitioning algorithm (LBTP); thirdly, we propose a time slice algorithm (TSA) for parallel simulation in each time cycle. To demonstrate the algorithm proposed in this paper, we built a simulation platform based on the combination of STK (Satellite Tool Kit), OPNET and Proxmox VE, and experimentally verified that the proposed architecture and algorithm significantly improve the simulation efficiency.

New formulations and experimental validation of non-stationary convolutions for the fast simulation of time-variant flowrates in ground heat exchangers

Flowrate control can have a significant positive impact on the thermal performance and economic profitability of ground-source heat pump systems. Including dynamic advective processes in the design phase, however, remains a challenging task, as few computationally efficient modeling tools allow for their adequate and accurate representation. The present work addresses this issue by presenting new formulations of non-stationary convolutions, an efficient simulation algorithm that relies on the theory of linear time-variant systems for predicting the thermal response of a ground heat exchanger to both dynamic heat loads and flow rates. First, the new original formulations are presented, which include (1) a simple time-domain expression and (2) a fast frequency-domain expression. Then, the efficiency and validity of the new formulations are verified using experimental multi-flowrate thermal response tests involving dynamic circulation, pumping and bleed flow rates in closed-loop and standing column well ground heat exchangers. Results show that the new formulations can reproduce the outlet fluid temperature of both experimental test cases with good accuracy ( MAE = 0.06 ∘ C and 0.26 ∘ C , respectively). At last, the high efficiency of the new frequency-domain expression is demonstrated, with the computing times ( 0.04 s and 0.01 s ) being 100 and 8 times faster than the original formulation in both scenarios.

An asynchronous simulation algorithm for post‐earthquake functional recovery of interdependent infrastructure systems

AbstractThe seismic resilience assessment of infrastructure systems not only requires evaluation of immediate damages after earthquakes, but also estimate of functional losses during the post‐earthquake recovery process. Infrastructure systems are interdependent during post‐earthquake recovery. To accurately consider this interdependence in resilience assessment, many studies have adopted post‐earthquake recovery simulation methods. In this study, an efficient asynchronous simulation algorithm is proposed, which does not require synchronous checking of the states of infrastructure elements at each time step, but rather asynchronously derives the functional recovery time of each element based on its dependency relationships. This study proves that the time complexity of the proposed asynchronous algorithm is proportional to the scale of the infrastructure system and is independent of the time step size. Furthermore, a case study of post‐earthquake recovery of a portfolio of buildings considering safety cordon areas is used to demonstrate that the asynchronous algorithm can significantly reduce the simulation time of large‐scale infrastructure systems compared to synchronous algorithms.

Collaborative and Adaptive Bayesian Optimization for bounding variances and probabilities under hybrid uncertainties

Uncertainty quantification (UQ) has been widely recognized as of vital importance for reliability-oriented analysis and design of engineering structures, and three groups of mathematical models, i.e., the probability models, the imprecise probability models and the non-probabilistic models, have been developed for characterizing uncertainties of different forms. The propagation of these three groups of models through expensive-to-evaluate simulators to quantify the uncertainties of outputs is then one of the core, yet highly challenging task in reliability engineering, as it involves a demanding double-loop numerical dilemma. For addressing this issue, the Collaborative and Adaptive Bayesian Optimization (CABO) has been developed in our previous work, but it only applies to imprecise probability models and is only capable of bounding the output expectation. We present a substantial improvement of CABO to incorporate all three categories of uncertainty models and to bound arbitrary probabilistic measures such as output variance and failure probability. The algorithm is based on a collaborative active learning mechanism, that is, jointly performing Bayesian optimization in the epistemic uncertainty subspace and Bayesian cubature in the aleatory uncertainty subspace, thus allowing to adaptively produce training samples in the joint uncertainty space. An efficient conditional Gaussian process simulation algorithm is embedded in CABO for acquiring training points and Bayesian inference in both uncertain subspaces. Benchmark studies show that CABO exhibits a remarkable performance in terms of numerical efficiency, accuracy, and global convergence.

Highly efficient and accurate algorithm for multiscale equivalent modeling and mechanical performance simulation of printed circuit boards

The increasing prevalence of reliability issues related to printed circuit boards (PCBs) has led to significant interest in applying finite element analysis (FEA) for PCBs. This study presents an equivalent property algorithm based on the mesoscale finite element method (FEM), which took into account the intricate structural features of conductive layers in PCBs. Additionally, a series of automated scripts were developed to streamline the modeling and calculating process, markedly enhancing the accuracy and efficiency of equivalent modeling and simulation of the PCB. Based on the proposed algorithm, a multiscale three-point bending finite element model of a PCB was established. By comparing the results to experimental data from three-point bending tests, it was demonstrated that the stress field simulated using the proposed algorithm accurately represented the anisotropic properties of the intricate copper traces in the conductive layers, leading to more accurate predictions of mechanical responses than those obtained using conventional algorithms. Furthermore, the research on the impact of partition size on both simulation accuracy and computational efficiency clearly demonstrated that partition size significantly affected these aspects. In conclusion, the proposed algorithm provided a more reliable and efficient approach for applying FEA and FEM to PCBs, contributing to advancements in the field of PCB analysis and fostering improvements in electronics design and manufacturing.

Macroscopic parking dynamics and equitable pricing: Integrating trip-based modeling with simulation-based robust optimization

Characterizing the integrated traffic and parking dynamics in a coupled road-parking system is challenging due to the interactions between parking and non-parking vehicles and the presence of cruising-for-parking. In this paper, we propose a new trip-based macroscopic model of parking considering various sources of user heterogeneity that are key to modeling parking. The macroscopic or network fundamental diagram is used to describe the accumulation-based network speed at which vehicles are traveling. An efficient event-based simulation algorithm is proposed as the resolution scheme for the trip-based model. Numerical results reveal that after calibration, the proposed trip-based model yields similar results to those obtained using an accumulation-based approach and microscopic simulation. Taking the trip-based model as the simulation engine, we formulate a simulation-based robust optimization problem of equitable duration-based parking pricing. To solve this problem with simulation stochasticity, a new global optimizer termed NoisyDIRECT is proposed as an open-source solution algorithm. This algorithm automatically identifies the level of simulation stochasticity for each decision vector across the search space, based on which the variable number of simulation runs per point is determined. Thus, the computational resource can be better allocated so that the optimal solution can be found in a more computationally efficient and reliable manner. Numerical results demonstrate that NoisyDIRECT provides better solutions than those yielded by fixed-number sample-path optimization and non-robust optimization methods.

Particle laden flows around a circular cylinder from the hydrodynamic to granular regime

The flow around a circular cylinder is studied with the presence of solid finite-size spherical particles. The simulations of suspension flows are based on highly efficient simulation algorithms to generate scalable Lattice Boltzmann Method calculations. Simulations of particle resolved dynamics with up to 4800 million grid cells and 1.8 million spherical particles are carried out for different flow regimes and particle concentrations (from dilute, 5% volume concentration to semi-dilute regime, 20%). We investigated a range of parameters that spans from suspensions of iso-dense particles to highly inertial particles in a granular flow, while considering particle to cylinder diameter ratios of 5 and 10.The flow response is described by characteristic properties such as vortex shedding frequency or wake recirculation length. Flow regimes with iso-dense particles can be rationalized by means of mixture material properties while we observe a progressive transition to granular flow for inertial particles. Drag and lift forces acting onto the cylinder are presented in time and frequency domains and the physical analysis is based on the respective hydrodynamic and particle collision contributions.

Conditional Uniformity and Hawkes Processes

Classic results show that the Hawkes self-exciting point process can be viewed as a collection of temporal clusters, in which exogenously generated initial events give rise to endogenously driven descendant events. This perspective provides the distribution of a cluster’s size through a natural connection to branching processes, but this is irrespective of time. Insight into the chronology of a Hawkes process cluster has been much more elusive. Here, we employ this cluster perspective and a novel adaptation of the random time change theorem to establish an analog of the conditional uniformity property enjoyed by Poisson processes. Conditional on the number of epochs in a cluster, we show that the transformed times are jointly uniform within a particular convex polytope. Furthermore, we find that this polytope leads to a surprising connection between these continuous state clusters and parking functions, discrete objects central in enumerative combinatorics and closely related to Dyck paths on the lattice. In particular, we show that uniformly random parking functions constitute hidden spines within Hawkes process clusters. This yields a decomposition that is valuable both methodologically and practically, which we demonstrate through application to the popular Markovian Hawkes model and proposal of a flexible and efficient simulation algorithm.

A High-Efficiency Spectral Element Method Based on CFS-PML for GPR Numerical Simulation and Reverse Time Migration

Improving the accuracy and efficiency of the numerical simulation of ground penetrating radar (GPR) becomes a pressing need with the rapidly increased amount of inversion data and the growing demand for migration imaging quality. In this article, we present a numerical spectral element time-domain (SETD) simulation procedure for GPR forward modeling and further apply it to the reverse time migration (RTM) with complex geoelectric models. This approach takes into account the flexibility of the finite element methods and the high precision of the spectral methods. Meanwhile, in this procedure, the complex frequency shifted perfectly matched layer (CFS-PML) is loaded to effectively suppress the echo at the truncated boundary, and the per-element GPU parallel framework used can achieve up to 5.7788 times the efficiency compared with the CPU calculation. The experiments on SETD spatial convergence and CFS-PML optimal parameter selection showed that, under the same degree of freedom, the SETD offered substantially better accuracy compared with the traditional FETD. The experiments on RTM of different profiles with different orders of SETD via a complex geoelectric model verify the universality of the algorithm. The results indicate that the RTM imaging effect has been significantly improved with the increase of SETD order. It fully proves the great potential of efficient and high-precision SETD simulation algorithm in the RTM imaging direction and shows certain guiding significance for underground target structure exploration.

Simulation of thermal behavior evolution in lithium-ion pouch cells using a coupled reaction-thermal-expansion model

Conventional lithium-ion battery thermal simulation methods establish a coupled heat generation/transfer model to calculate the temperature evolution for hard-pack cells. However, the thermal behavior evolution of a pouch cell includes multi-processes such as heat generation, gas production, heat transfer, and pouch expansion. There is currently a lack of computational models for predicting the multi-state parameter evolution in temperature and pressure. In this paper, an efficient and accurate thermal behavior simulation algorithm for lithium-ion pouch cells is developed. A heated/exothermic two-stage hypothesis for the thermal behavior evolution in pouch cells was established under thermal abuse conditions. Analytical models of exothermic reaction, heat transfer and pouch expansion were formulated and integrated into a unified thermal behavior analysis framework. The proposed algorithm simulated the thermal behavior evolution in four commercial lithium-ion pouch cells. The computational and experimental results showed the calculation errors of the cell temperature and gas volume for the four samples were less than 3% and 5%, respectively. In terms of efficiency, the calculation time of the algorithm is within milliseconds. The accuracy and efficiency of the proposed algorithm are verified.

Explicit Calculation of Structural Commutation Relations for Stochastic and Dynamical Graph Grammar Rule Operators in Biological Morphodynamics.

Many emergent, non-fundamental models of complex systems can be described naturally by the temporal evolution of spatial structures with some nontrivial discretized topology, such as a graph with suitable parameter vectors labeling its vertices. For example, the cytoskeleton of a single cell, such as the cortical microtubule network in a plant cell or the actin filaments in a synapse, comprises many interconnected polymers whose topology is naturally graph-like and dynamic. The same can be said for cells connected dynamically in a developing tissue. There is a mathematical framework suitable for expressing such emergent dynamics, "stochastic parameterized graph grammars," composed of a collection of the graph- and parameter-altering rules, each of which has a time-evolution operator that suitably moves probability. These rule-level operators form an operator algebra, much like particle creation/annihilation operators or Lie group generators. Here, we present an explicit and constructive calculation, in terms of elementary basis operators and standard component notation, of what turns out to be a general combinatorial expression for the operator algebra that reduces products and, therefore, commutators of graph grammar rule operators to equivalent integer-weighted sums of such operators. We show how these results extend to "dynamical graph grammars," which include rules that bear local differential equation dynamics for some continuous-valued parameters. Commutators of such time-evolution operators have analytic uses, including deriving efficient simulation algorithms and approximations and estimating their errors. The resulting formalism is complementary to spatial models in the form of partial differential equations or stochastic reaction-diffusion processes. We discuss the potential application of this framework to the remodeling dynamics of the microtubule cytoskeleton in cortical microtubule networks relevant to plant development and of the actin cytoskeleton in, for example, a growing or shrinking synaptic spine head. Both cytoskeletal systems underlie biological morphodynamics.

High-fidelity robust and efficient finite difference algorithm for simulation of polymer-induced turbulence in cylindrical coordinates

A high-fidelity finite difference method for simulation of viscoelastic turbulence in cylindrical coordinates has been developed. In this algorithm, the evolution equation for the polymer conformation tensor is discretized by an improved Kurganov–Tadmor (KT) scheme, while the continuity and momentum equations are discretized by a well-established second-order accurate central difference scheme on staggered grids. To minimize the computational cost of the original KT scheme, two novel improvements have been implemented: (i) a sequential ranking strategy to rapidly select the slope-approximation in the original KT scheme, and (ii) a simple criterion to diagnose and in turn assure the positive definiteness of the conformation tensor that obviates the need for CPU intensive eigenvalue calculations. These modifications lead to an algorithm that is twice as fast as the original KT method. This in turn has enabled high-fidelity and efficient larger-scale simulations of viscoelastic turbulent flows in a parallel framework using 2D-domain decomposition, as evinced by benchmark results for two canonical viscoelastic flows, namely, the viscoelastic pipe flows (VPF) and the viscoelastic Taylor–Couette flows (VTCF). To that end, the fidelity and computational efficiency of this algorithm have been demonstrated in a broad range of polymer-induced flow dynamics, namely, elasto-inertial instability in the VPF, polymer-induced drag reduction at high R e in the VTCF, and purely elastic instability in low- R e VTCF. Overall, this algorithm is an efficient and high-fidelity scheme for the simulation of viscoelastic turbulence in a broad range of elastically and/or inertially dominated flows. • A new robust finite-difference method for viscoelastic turbulence is proposed. • High-fidelity simulations are performed using the proposed method. • Strategies to improve computational efficiency are proposed. • Comprehensive validations in cylindrical coordinates are carried out.

A framework for conditional simulation of nonstationary non-Gaussian random field and multivariate processes

We propose a conditional simulation framework for the nonstationary/nonhomogeneous non-Gaussian/Gaussian field and multivariate processes. There are three distinct stages within the proposed framework. The first one is to sample directly the unconditional nonstationary non-Gaussian field or multivariate processes without resorting to the underlying Gaussian characteristics. The second stage maps the unconditional samples and the conditions (i.e., observations) to the Gaussian space and uses them to find the conditional mean and covariance. In the final stage, the conditional samples by considering the effect of the conditioning are generated in Gaussian space and mapped back to the original non-Gaussian field. The efficiency of the framework relies on the efficient unconditional simulation algorithms (i.e., the iterative rank-dependent shuffling algorithm, and the iterative power and amplitude correction algorithm), and the reuse of the unconditional samples for the conditional simulation. The conditional samples match the conditional mean and conditional variance–covariance. The framework can directly apply to the random field and multivariate processes defined by the marginal probability distribution function and the covariance-variance information, or the time–frequency dependent PSD function. The framework is illustrated using several examples.

How to Simulate Quantum Measurement without Computing Marginals

We describe and analyze algorithms for classically simulating measurement of an n-qubit quantum state in the standard basis, that is, sampling a bit string from the probability distribution determined by the Born rule. Our algorithms reduce the sampling task to computing poly(n) amplitudes of n-qubit states; unlike previously known techniques they do not require computation of marginal probabilities. Two classes of quantum states are considered: output states of polynomial-size quantum circuits, and ground states of local Hamiltonians with an inverse polynomial spectral gap. We show that our algorithms can significantly accelerate quantum circuit simulations based on tensor network contraction or low-rank stabilizer decompositions. As another striking consequence we obtain the first efficient classical simulation algorithm for measurement-based quantum computation with the surface code resource state on any planar graph and any schedule of measurements.

SITH: An R package for visualizing and analyzing a spatial model of intratumor heterogeneity.

Cancer progression, including the development of intratumor heterogeneity, is inherently a spatial process. Mathematical models of tumor evolution may be a useful starting point for understanding the patterns of heterogeneity that can emerge in the presence of spatial growth. A commonly studied spatial growth model assumes that tumor cells occupy sites on a lattice and replicate into neighboring sites. Our R package SITH provides a convenient interface for exploring this model. Our efficient simulation algorithm allows for users to generate 3D tumors with millions of cells in under a minute. For visualizing the distribution of mutations throughout the tumor, SITH provides interactive graphics and summary plots. Additionally, SITH can produce synthetic bulk and single-cell DNA-seq datasets by sampling from the simulated tumor. A streamlined API makes SITH a useful tool for investigating the relationship between spatial growth and intratumor heterogeneity. SITH is a part of CRAN and can be installed by running install.packages("SITH") from the R console. See https://CRAN.R-project.org/package=SITH for the user manual and package vignette.

Effect of partial distinguishability on quantum supremacy in Gaussian Boson sampling

Gaussian boson sampling (GBS) allows for a way to demonstrate quantum supremacy with the relatively modest experimental resources of squeezed light sources, linear optics, and photon detection. In a realistic experimental setting, numerous effects can modify the complexity of the sampling, in particular loss, partial distinguishability of the photons, and the use of threshold detectors rather than photon counting detectors. In this paper, we investigate GBS with partial distinguishability using an approach based on virtual modes and indistinguishability efficiency. We develop a model using these concepts and derive the probabilities of measuring a specific output pattern from partially distinguishable and lossy GBS for both types of detectors. In the case of threshold detectors, the probability as calculated by the Torontonian is a special case under our framework. By analyzing the expressions of these probabilities we propose an efficient classical simulation algorithm which can be used to calculate the probabilities. Our model and algorithm provide foundations for an approximate method for calculating probabilities. It also allows for a way to design sampling algorithms that are not only compatible with existing algorithms for ideal GBS, but can also reduce their complexity exponentially, depending on the indistinguishability efficiency. Using this we show how the boundary of quantum supremacy in GBS can be affected by partial distinguishability.

Hybrid quantum-classical algorithms for approximate graph coloring

We show how to apply the recursive quantum approximate optimization algorithm (RQAOA) to MAX-k-CUT, the problem of finding an approximate k-vertex coloring of a graph. We compare this proposal to the best known classical and hybrid classical-quantum algorithms. First, we show that the standard (non-recursive) QAOA fails to solve this optimization problem for most regular bipartite graphs at any constant level p: the approximation ratio achieved by QAOA is hardly better than assigning colors to vertices at random. Second, we construct an efficient classical simulation algorithm which simulates level-1 QAOA and level-1 RQAOA for arbitrary graphs. In particular, these hybrid algorithms give rise to efficient classical algorithms, and no benefit arising from the use of quantum mechanics is to be expected. Nevertheless, they provide a suitable testbed for assessing the potential benefit of hybrid algorithm: We use the simulation algorithm to perform large-scale simulation of level-1 QAOA and RQAOA with up to 300 qutrits applied to ensembles of randomly generated 3-colorable constant-degree graphs. We find that level-1 RQAOA is surprisingly competitive: for the ensembles considered, its approximation ratios are often higher than those achieved by the best known generic classical algorithm based on rounding an SDP relaxation. This suggests the intriguing possibility that higher-level RQAOA may be a potentially useful algorithm for NISQ devices.

A Language for Agent-based Discrete-event Modeling and Simulation of Linked Lives

In agent-based modeling and simulation, discrete-time methods prevail. While there is a need to cover the agents’ dynamics in continuous time, commonly used agent-based modeling frameworks offer little support for discrete-event simulation. Here, we present a formal syntax and semantics of the language ML3 (Modeling Language for Linked Lives) for modeling and simulating multi-agent systems as discrete-event systems. The language focuses on applications in demography, such as migration processes, and considers this discipline’s specific requirements. These include the importance of life courses being linked and the age-dependency of activities and events. The developed abstract syntax of the language combines the metaphor of agents with guarded commands. Its semantics is defined in terms of Generalized Semi-Markov Processes. The concrete language has been realized as an external domain-specific language. We discuss implications for efficient simulation algorithms and elucidate benefits of formally defining domain-specific languages for modeling and simulation.