Simulation Of Complex Systems Research Articles

Recent advances in quantum fragmentation approaches to complex molecular and condensed‐phase systems

AbstractQuantum mechanical (QM) calculations are critical in quantitatively understanding the relationship between the structure and physicochemical properties of various chemical systems. However, the sharply increasing computational cost with the system size has severely hindered applying direct QM calculations on large‐sized systems. Hence, linear‐scaling and/or fragmentation QM methods have been proposed to overcome this difficulty. In this review, we focus on the recent development and applications of the electrostatically embedded generalized molecular fractionation with the conjugate caps (EE‐GMFCC) method in probing various properties of complex large molecules and condensed‐phase systems. The EE‐GMFCC method is now capable of describing the localized excited states of biomolecules and molecular crystals with a chromophore. The EE‐GMF method is also combined with anharmonic vibrational calculations for accurate simulation of the infrared spectrum of the magic number H+(H2O)21 cluster at the coupled cluster level. With an adaptive fragmentation scheme, the EE‐GMF‐based ab initio molecular dynamics is able to directly simulate chemical reactions occurred in atmospheric molecular clusters. Furthermore, by combining the EE‐GMF(CC) method and deep machine learning techniques, neural network potentials can be efficiently constructed for accurate simulations of complex systems with the accuracy of high‐level wave function methods. The EE‐GMF(CC) method is expected to become a practical tool for quantitative description of complex large molecules and condensed‐phase systems with high‐level ab initio theories or ab initio quality potentials.This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Reaction Mechanisms and Catalysis

Efficient Computation of Electromagnetic Coupling From Various Types of Antennas Based on Reciprocity Theorem

In the design of complex electronic systems, such as electric vehicles, electromagnetic coupling between a specific component and an antenna should be quantitatively estimated according to the radiated emission (RE) and radiated immunity (RI) standards. However, full-wave electromagnetic simulations of complex systems and antennas in a large space take much time and effort. The decomposition method based on reciprocity and Huygens's principle, which enables the separate analysis of source and victim parts, has been previously proposed for efficient analysis of coupling to a lumped port of the victim antenna. In this article, the decomposition method along with the extraction of an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> -parameter block is extended to handle the dominant transverse electric mode in a waveguide antenna. The proposed method removes the difficulty and uncertainty in the modeling process of a commercial waveguide antenna. Also, while applying the decomposition method, Huygens's principle is additionally applied to further reduce the full-wave simulation time of a large empty space. The proposed methods are validated with full-wave electromagnetic simulations, and also experimentally applied to RI estimation. The proposed time-efficient techniques can realize the full-wave computation of RE and RI for real complicated systems and commercial antennas.

An efficient, robust and reliable initialization of phase fractions in Rachford-Rice system of equations for three-phase split calculation

Multiphase flash calculations are crucial in compositional and thermal simulations for complex fluid systems in which three phases may co-exist. Solution of the Richford-Rice system of equations is an important operation in the multiphase flash. Within the literature, solution methods have been proposed, based on optimization methods and on nonlinear equation solution methods. Many methods in these two approaches may fail if the initial estimate is too inaccurate, and may require a substantial amount of computation only to arrive at a trivial solution in cases where the assumed number of phases is too high. The present work deals with an initialization method for the solution of the Rachford-Rice system of equations based on approximating each of the functions by means of tangent lines. It is proposed to solve the system of equations by applying a two-step Newton-type method. Through extensive numerical experimentation, the efficiency, robustness and reliability of the proposed initialization method for the solution of the Rachford-Rice system of equations by means of a Newton-type method is shown.

Are general circulation models obsolete?

Traditional general circulation models, or GCMs—that is, three-dimensional dynamical models with unresolved terms represented in equations with tunable parameters—have been a mainstay of climate research for several decades, and some of the pioneering studies have recently been recognized by a Nobel prize in Physics. Yet, there is considerable debate around their continuing role in the future. Frequently mentioned as limitations of GCMs are the structural error and uncertainty across models with different representations of unresolved scales and the fact that the models are tuned to reproduce certain aspects of the observed Earth. We consider these shortcomings in the context of a future generation of models that may address these issues through substantially higher resolution and detail, or through the use of machine learning techniques to match them better to observations, theory, and process models. It is our contention that calibration, far from being a weakness of models, is an essential element in the simulation of complex systems, and contributes to our understanding of their inner workings. Models can be calibrated to reveal both fine-scale detail and the global response to external perturbations. New methods enable us to articulate and improve the connections between the different levels of abstract representation of climate processes, and our understanding resides in an entire hierarchy of models where GCMs will continue to play a central role for the foreseeable future.

Innovations in integrating machine learning and agent-based modeling of biomedical systems

Agent-based modeling (ABM) is a well-established computational paradigm for simulating complex systems in terms of the interactions between individual entities that comprise the system’s population. Machine learning (ML) refers to computational approaches whereby algorithms use statistical methods to “learn” from data on their own, i.e., without imposing any a priori model/theory onto a system or its behavior. Biological systems—ranging from molecules, to cells, to entire organisms, to whole populations and even ecosystems—consist of vast numbers of discrete entities, governed by complex webs of interactions that span various spatiotemporal scales and exhibit nonlinearity, stochasticity, and variable degrees of coupling between entities. For these reasons, the macroscopic properties and collective dynamics of biological systems are generally difficult to accurately model or predict via continuum modeling techniques and mean-field formalisms. ABM takes a “bottom-up” approach that obviates common difficulties of other modeling approaches by enabling one to relatively easily create (or at least propose, for testing) a set of well-defined “rules” to be applied to the individual entities (agents) in a system. Quantitatively evaluating a system and propagating its state over a series of discrete time-steps effectively simulates the system, allowing various observables to be computed and the system’s properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, at least in an unbiased way, there is a uniquely synergistic opportunity to employ ML to help infer optimal, system-specific ABM rules. Once such rule-sets are devised, running ABM calculations can generate a wealth of data, and ML can be applied in that context too—for example, to generate statistical measures that accurately and meaningfully describe the stochastic outputs of a system and its properties. As an example of synergy in the other direction (from ABM to ML), ABM simulations can generate plausible (realistic) datasets for training ML algorithms (e.g., for regularization, to mitigate overfitting). In these ways, one can envision a variety of synergistic ABM⇄ML loops. After introducing some basic ideas about ABMs and ML, and their limitations, this Review describes examples of how ABM and ML have been integrated in diverse contexts, spanning spatial scales that include multicellular and tissue-scale biology to human population-level epidemiology. In so doing, we have used published studies as a guide to identify ML approaches that are well-suited to particular types of ABM applications, based on the scale of the biological system and the properties of the available data.

Book Reviews [6 Books Reviewed

The following six books are reviewed: High Voltage Electrical Insulation Engineering, 2nd Edition (R. Arora and W. Mosch; 2022); Batteries—Materials Principles and Characterization Methods (C. Liao; 2021); Practical Terahertz Electronics: Devices and Applications—Volume One (V. K. Khanna; 2021); Simulation of Complex Systems (A. Argun, et al; 2021); Elegant Circuits—Simple Chaotic Oscillators (J. C. Sprott and W. J. Thio; 2022); and Power System Protection, 2nd Edition (P. M. Anderson, et al; 2022).

Utility and Feasibility of a Low-Cost System to Simulate Clipping Strategy for Cerebral Aneurysms Using Three-Dimensional Computed Tomography Angiography with Virtual Craniotomy

To assess utility and feasibility of a low-cost system to simulate clipping strategy for cerebral aneurysms using patient-specific surgically oriented three-dimensional (3D) computed tomography angiography with virtual craniotomy. From 2017 to 2021, 53 consecutive patients scheduled for aneurysm clipping underwent preoperative planning using 3D computed tomography angiography with virtual craniotomy. The model was oriented in the surgical position to observe the anatomy through surgical corridors. Clipping was planned considering 3 parameters: shape of the clip, clip type (standard vs. fenestrated), and clipping strategy (simple vs. multiple). We used a scoring system (0-3) to assess the concordance of virtual planning with real surgery by assigning 1 point for each correctly predicted parameter. Qualitative assessment of 3D models was a secondary end point. In 51 patients, 3D images perfectly matched the real anatomy shown in surgical videos. Concordance scores of 0, 1, 2, and 3 occurred with a frequency of 5%, 14%, 38%, and 43%, respectively. Concerning the shape of the clip, clip type, and clipping strategy, the concordance occurred in 73%, 80%, and 59%, respectively. Compared with simple clipping, strategies with multiple clippings were more difficult to predict correctly. Concordance scores of 0, 1, 2, and 3 occurred with a frequency of 5.7%, 5.7%, 31.4%, and 57.1%, respectively, in simple clipping and 4.8%, 28.6%, 47.6%, and 19%, respectively, in multiple clipping. In our experience, use of 3D computed tomography angiography with virtual craniotomy is an easy and useful solution to plan clipping strategy. The surgeon's awareness of the surgical anatomy is improved. Although this method has some technical limitations, it represents a low-cost alternative if complex and expensive simulation systems are not available.

On the combination of dynamic substructuring and surrogate modeling for the design and study of complex systems

For decades, dynamic substructuring approaches have been successfully utilized for the modeling and simulation of complex systems. More recently, surrogate modeling methods have emerged as a way to reduce the computational cost of simulating for design. This talk will introduce a paradigm in which these two techniques may be combined for the design and study of complex systems. For illustration, a simple example is put forward in which a mass-spring system is broken into subsystems and parameterized. The open-source Python package SMT (Surrogate Modeling Toolbox) developed and maintained by Bouhel et al. is used to construct surrogate models for each subsystem in which the component parameters are taken as inputs and the frequency response functions (FRFs) of the interface degrees of freedom are output. These FRFs are then used to construct the response of the fully coupled system by utilizing a dual Frequency Based Substructuring technique. In many real-world applications, this manner of calculating the effect of varying design parameters on the system response is likely more practical and computationally feasible than the alternative of directly simulating the coupled system response for many parameter sets and training a surrogate model from that output for further design space exploration and optimization. [Work supported by the Office of Naval Research]

Committor-Consistent Variational String Method.

The treatment of slow and rare transitions in the simulation of complex systems poses a great computational challenge. A powerful approach to tackle this challenge is the string method, which represents the transition path as a one-dimensional curve in a multidimensional space of collective variables. Commonly used strategies for pathway optimization include aligning the tangent of the string to the local mean force or to the mean drift determined from swarms of short trajectories. Here, a novel strategy is proposed, allowing the string to be optimized based on a variational principle involving the unidirectional reactive flux expressed in terms of the time-correlation function of the committor. The method is illustrated with model systems and then probed with the alanine dipeptide and a coarse-grained model of the barstar-barnase protein complex. Successive iterations variationally refine the string toward an optimal transition pathway following the gradient of the committor between two metastable states.

Distributed Consensus Based and Network Economic Control of Energy Internet Management

Energy internet (EI) was proposed to improve the utilization of multiple energy and meet the growing demand for energy. This paper proposes the distributed consensus control algorithm combined with a multi-agent system (MAS) which is applied to distributed generators in the energy internet. By selecting the incremental cost (IC) of each generation unit as the consensus variable, the algorithm is able to solve the conventional centralized economic dispatch (ED) problem in a distributed scheduling manner. The proposed algorithm is veriðed in the MAS layer and through a simulation model of the EI network in the MATLAB software. Simulation results conclude that incremental cost converges to its optimal value whether load demand is varying or generators plug-and-play. Distributed consensus control algorithm can provide better service for EI, it is immune to topological variations and accommodate desired plug-and-play features, and it enables real-time modeling and simulation of complex power systems.

Data-Driven Many-Body Potential Energy Functions for Generic Molecules: Linear Alkanes as a Proof-of-Concept Application.

We present a generalization of the many-body energy (MB-nrg) theoretical/computational framework that enables the development of data-driven potential energy functions (PEFs) for generic covalently bonded molecules, with arbitrary quantum mechanical accuracy. The "nearsightedness of electronic matter" is exploited to define monomers as "natural building blocks" on the basis of their distinct chemical identity. The energy of generic molecules is then expressed as a sum of individual many-body energies of incrementally larger subsystems. The MB-nrg PEFs represent the low-order n-body energies, with n = 1-4, using permutationally invariant polynomials derived from electronic structure data carried out at an arbitrary quantum mechanical level of theory, while all higher-order n-body terms (n > 4) are represented by a classical many-body polarization term. As a proof-of-concept application of the general MB-nrg framework, we present MB-nrg PEFs for linear alkanes. The MB-nrg PEFs are shown to accurately reproduce reference energies, harmonic frequencies, and potential energy scans of alkanes, independently of their length. Since, by construction, the MB-nrg framework introduced here can be applied to generic covalently bonded molecules, we envision future computer simulations of complex molecular systems using data-driven MB-nrg PEFs, with arbitrary quantum mechanical accuracy.

Contrastive Learning of Coarse-Grained Force Fields.

Coarse-grained models have proven helpful for simulating complex systems over long time scales to provide molecular insights into various processes. Methodologies for systematic parametrization of the underlying energy function or force field that describes the interactions among different components of the system are of great interest for ensuring simulation accuracy. We present a new method, potential contrasting, to enable efficient learning of force fields that can accurately reproduce the conformational distribution produced with all-atom simulations. Potential contrasting generalizes the noise contrastive estimation method with umbrella sampling to better learn the complex energy landscape of molecular systems. When applied to the Trp-cage protein, we found that the technique produces force fields that thoroughly capture the thermodynamics of the folding process despite the use of only α-carbons in the coarse-grained model. We further showed that potential contrasting could be applied over large data sets that combine the conformational ensembles of many proteins to improve force field transferability. We anticipate potential contrasting as a powerful tool for building general-purpose coarse-grained force fields.

Адаптивный подход к реализации сложных систем моделирования

The paper presents an approach potentially capable of implementing efficient import substitution in the field of modern software systems for simulating and designing manufacturing processes in various industries, as well as of compensating for the destruction of international connections that made it possible to build efficient labor division structures for developing modern software systems. To solve these problems, we propose to implement an adaptive simulation system than can adjust to different domains by means of considerable automation concerning development of domain-specific programming languages describing models in the relevant subject areas. We also propose to split development into the following competence and responsibility zones: developing an extensible core for the adaptive simulation system and shared language and visual (graphic) components; developing domain-specific extensions, which may include both language and visual (graphic) components; populating a specific extension with a database of industry standards; developing a manufacturing model specifically. Using a shared platform represented by the adaptive simulation system allows for reusing extensions from different domains so as to form complex simulations, resulting in possible technological breakthroughs at the juxtaposition of various industries. The paper also briefly describes certain risks associated with the implementation and maintenance of complex simulation systems in the Russian Federation. We consider alternatives to the solutions proposed

Performance of a Novel Enhanced Sparrow Search Algorithm for Engineering Design Process: Coverage Optimization in Wireless Sensor Network

Burgeoning swarm intelligence techniques have been creating a feasible theoretical computational method for the modeling, simulation, and optimization of complex systems. This study aims to increase the coverage of a wireless sensor network (WSN) and puts forward an enhanced version of the sparrow search algorithm (SSA) as a processing tool to achieve this optimization. The enhancement of the algorithm covers three aspects. Firstly, the Latin hypercube sampling technique is utilized to generate the initial population to obtain a more uniform distribution in the search space. Secondly, a sine cosine algorithm with adaptive adjustment and the Lévy flight strategy are introduced as new optimization equations to enhance the convergence efficiency of the algorithm. Finally, to optimize the individuals with poor fitness in the population, a novel mutation disturbance mechanism is introduced at the end of each iteration. Through numerical tests of 13 benchmark functions, the experimental results show that the proposed enhanced algorithm can converge to the optimum faster and has a more stable average value, reflecting its advantages in convergence speed, robustness, and anti-local extremum ability. For the WSN coverage problem, this paper established a current optimization framework based on the swarm intelligence algorithms, and further investigated the performance of nine algorithms applied to the process. The simulation results indicate that the proposed method achieves the highest coverage rate of 97.66% (on average) among the nine algorithms in the calculation cases, which is increased by 13.00% compared with the original sparrow search algorithm and outperforms other methods by 1.47% to 15.34%.

Finding the Balance Between Simplicity and Realism in Participatory Modeling for Environmental Planning

Complex systems simulations can support collaborative water planning by allowing stakeholders to jointly see hidden effects of land- and water-use decisions on groundwater flow. We adopted a participatory modeling progression where stakeholders learned to modify and use increasingly sophisticated models to assess policy impacts on groundwater levels. Stakeholders' shared understanding of the problem and the novelty, concreteness, and richness of proposed solutions evolved alongside the models’ degree of realism, but up to a certain point. More realistic models became a distraction and stymied efforts to plan for water shortages. The reflective learning required to plan for complex environmental problems is best supported by models that strike a balance between representational fidelity and end-user intelligibility. Complicated models and high-resolution data may overwhelm model users, preventing them from acting on the useful planning insights they derived from the exploratory modeling, particularly within social contexts that exhibit strong power dynamics and favor prediction.

A multi-ecological complex fungal growth prediction model based on Cellular Automata and interval estimation

As the decomposer of ecosystems, fungi play an indispensable part in the carbon cycle and energy cycle. We studied the impact of biodiversity and variation probability on decomposition efficiency with the mild climate data [1-2] with the semi-arid climate, and got qualitative conclusions. The establishment of this model follows the principle of total simplicity to complexity. In the process of continuously adding influencing factors to the model, the overall agreement of the results obtained from the various parts of the model is still maintained. And the conclusion of the model is basically similar to the actual situation. The feasibility of using Cellular Automata to solve the model has also been verified by past experience in the simulation of complex systems [3].

Smart and Dynamic Indoor Evacuation System (SDIES)

The paper presents a complex simulation system for demonstrating the evacuation process in a building, whereby people attempt to escape from a dangerous scenario. It is novel in that it integrates a range of different models: agent-based model, social force model, and psychological behaviour with emotions and norms. The method uses the communication network based on the message queuing telemetry transport protocol that assists to gather information from the environment. The paths are modified using feelings and rule-based expert system. The authors conduct some simulations and conclude with recommendations for management of safer environments.

Research on co-simulation of multi-resolution models based on HLA

Intelligence, hierarchy, and multi-resolution (MR) modeling are vital to the complex system simulation. Based on the proposed MR-Agent-Discrete EVent System (MR-Agent-DEVS) specification, this paper discusses the co-simulation of MR models under high-level architecture (HLA). First, the structure and formal description of MR-Agent-DEVS federate models were proposed. The communication mechanism and simulation process of MR-Agent-DEVS federate models were analyzed. Finally, an example of distributed interactive simulation for emergency material supply was given. The results of the simulation test show that HLA enhances the reusability of MR-Agent-DEVS models, knowledge updating and model coupling enrich the interoperability of MR-Agent-DEVS models, and the real-time memory of knowledge base in Agent ensures the consistency of MR-Agent-DEVS models in aggregation and disaggregation.

Imagining the School of the Future Through Computational Simulations: Scenarios’ Sustainability and Agency as Keywords

Computational simulations are fundamental tools not only for scientific research but also for education. They are frequently used as virtual laboratories to foster students’ understanding of the theoretical concepts that lie at the basis of the simulated systems. Recent research works in STEM education have started to explore the potential of simulations as future-oriented objects, to support students in the development of future scenarios for real-world situations. In this paper, we present a teaching-learning module targeted to upper high-school students on simulations of complex systems. The peculiarity of this course is that, guiding the students through the conceptual and epistemological analysis of some computational agent-based models, we were able to ground on these disciplinary bases the introduction of key concepts of the futures studies, like that of scenario. More specifically, in this paper we address an original future-oriented activity in which the students were required to choose an urgent problem of their interest, imagine possible and desirable scenarios based on a simulation and identify the sequence of actions to be undertaken to reach the preferable future. In presenting the results of the module’s implementation we focus on two groups of students who spontaneously decided to address a problem related to the current educational system. In particular, we discuss how the future-oriented activity based on simulations led the students (i) to imagine sustainable scenarios for the school of the future, in which a dynamical equilibrium between opposite tensions is achieved, without any of them being eliminated and (ii) to recognize themselves as agents of transformation in a public, professional, and personal dimension.

Визначення змін характеру руху об'єктів ракетно-космічної техніки за авторегресійними моделями в умовах нерівновіддалених спостережень

The problem of increasing prediction accuracy for the motion of Earth-orbiting objects (EOOs) and detecting changes therein is topical for the tasks of spacecraft life prediction, space debris cataloguing, and navigation. Therefore, the problem of detecting changes in dynamic systems characterized by non-equidistant observations is topical. The purpose of this work is the development of autoregressive models with observations non-equidistant in time to detect changes in EOO motion. The methods employed are multivariate statistical analysis, time series prediction, and complex-system simulation under structural uncertainty. Data generated by NORAD (USA) were used as initial observations to describe EOO motion. They are actual, constantly updated, and freely available via the Internet. These data are presented in the Two-Line Element (TLE) format, which is a data format encoding a list of orbital elements of an EOO for a given point in time. This paper presents a method for constructing autoregressive models to describe the dynamics of EOOs represented by time series of TLE elements with values non-equidistant in time. On its basis, autoregressive models of the Sich-2 spacecraft’s dynamics were constructed. The standard errors of the models were analysed on examination samples, and significant deviations of the standard errors for the basic variables (apogee, perigee, eccentricity, longitude of ascending node, perigee argument, and average anomaly) were found, thus demonstrating changes in the Sich-2 motion from its basic regime. The novelty of this work lies in that the problem of detecting changes in EOO motion characteristics based on the proposed type of autoregressive models has not been considered before. Its practical value lies in that the simulation of the Sich-2 motion using time series of TLE elements allows one to detect changes in motion regimes; the method may be used in detecting in-service changes in EOO properties.