Simulation Of Complex Systems Research Articles

Phase Coexistence in Hamiltonian Hybrid Particle-Field Theory Using a Multi-Gaussian Approach.

This study introduces an implementation of multiple Gaussian filters within the Hamiltonian hybrid particle-field (HhPF) theory, aimed at capturing phase coexistence phenomena in mesoscopic molecular simulations. By employing a linear combination of two Gaussians, we demonstrate that HhPF can generate potentials with attractive and steric components analogous to Lennard-Jones (LJ) potentials, which are crucial for modeling phase coexistence. We compare the performance of this method with the multi-Gaussian core model (MGCM) in simulating liquid-gas coexistence for a single-component system across various densities and temperatures. Our results show that HhPF effectively captures detailed information on phase coexistence and interfacial phenomena, including microconfiguration transitions and increased interfacial fluctuations at higher temperatures. Notably, the phase boundaries obtained from HhPF simulations align more closely with those of LJ systems compared to the MGCM results. This work advances the hybrid particle-field methodology to address phase coexistence without requiring modifications to the equation of state or introducing additional interaction energy functional terms, offering a promising approach for mesoscale molecular simulations of complex systems.

HARNESSING BLOCKCHAIN WITH ENSEMBLE DEEP LEARNING-BASED DISTRIBUTED DOS ATTACK DETECTION IN IOT-ASSISTED SECURE CONSUMER ELECTRONICS SYSTEMS

Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques.

Automatic construction of Petri net models for computational simulations of molecular interaction network

Petri nets are commonly applied in modeling biological systems. However, construction of a Petri net model for complex biological systems is often time consuming, and requires expertise in the research area, limiting their application. To address this challenge, we developed GINtoSPN, an R package that automates the conversion of multi-omics molecular interaction network extracted from the Global Integrative Network (GIN) into Petri nets in GraphML format. These GraphML files can be directly used for Signaling Petri Net (SPN) simulation. To demonstrate the utility of this tool, we built a Petri net model for neurofibromatosis type I. Simulation of NF1 gene knockout, compared to normal skin fibroblast cells, revealed persistent accumulation of Ras-GTPs as expected. Additionally, we identified several other genes substantially affected by the loss of NF1’s function, exhibiting individual-specific variability. These results highlight the effectiveness of GINtoSPN in streamlining the modeling and simulation of complex biological systems.

Interdisciplinary Perspectives on Agent-Based Modeling in the Architecture, Engineering, and Construction Industry: A Comprehensive Review

This paper explores the transformative impact of agent-based modeling (ABM) on the architecture, engineering, and construction (AEC) industry, highlighting its indispensable role in revolutionizing project management, construction processes, safety protocols, and sustainability initiatives including energy optimization and occupants’ comfort. Through an in-depth review of 178 documents published between 1970 and 2024 on current practices and the integration of ABM with emerging digital technologies, this study underscores the critical importance of ABM in facilitating enhanced decision-making, resource optimization, and complex system simulations. For instance, ABM is shown to reduce project delays by up to 15% through enhanced resource allocation and improve safety outcomes by simulating worker behavior and identifying potential hazards in dynamic construction environments. The results reveal ABM’s potential to significantly improve construction methodologies, integrate technological advancements seamlessly, and contribute to the development of sustainable and resilient building practices. Furthermore, this paper identifies key areas for future research, including the exploration of ABM’s capabilities in conjunction with other digital innovations to unlock new avenues for efficiency and sustainability in construction. This study sets out a forward-looking agenda for providing this modeling approach to address contemporary challenges and harness opportunities for innovation and growth in the AEC sector.

AUTOMATED GESTURE RECOGNITION USING APPLIED LINGUISTICS WITH DATA-DRIVEN DEEP LEARNING FOR ARABIC SPEECH TRANSLATION

Gesture recognition for Arabic speech translation includes developing advanced technologies that correctly translate body and hand movements corresponding to Arabic sign language (ArSL) into spoken Arabic. This leverages machine learning and computer vision techniques in complex systems simulation platforms to scrutinize the gestures utilized in ArSL, detecting mild differences in facial expressions, hand shapes, and movements. Sign Language Recognition (SLR) is paramount in assisting communication for the Deaf and Hard-of-Hearing communities. It includes using vision-based methods and Surface Electromyography (sEMG) signals. The sEMG signal is crucial for recognizing hand gestures and capturing muscular activities in sign language. Researchers have comprehensively shown the capability of EMG signals to approach specific details, mainly in classifying hand gestures. This progression is a stimulating feature in extracting the interpretation and recognition of sign languages and investigating the phonology of signed language. Leveraging machine learning algorithms and signal processing techniques in complex systems simulation platforms, researchers aim to extract relevant traits from the sEMG signals that correspond to different ArSL gestures. This study introduces an Enhanced Dwarf Mongoose Algorithm with a Deep Learning-Driven Arabic Sign Language Detection (EDMODL-ASLD) technique on sEMG data. In the initial phase, the presented EDMODL-ASLD model is subjected to data preprocessing to change the input sEMG data into an attuned format. In the next stage, feature extraction with fractal theories is used to gather relevant and nonredundant data from the EMG window to construct a feature vector. In this study, the absolute envelope (AE), energy (E), root-mean square (RMS), standard deviation (STD), and mean absolute value (MAV) are the five time-domain extracted features for the EMG window observation. Meanwhile, the dilated convolutional long short-term memory (ConvLSTM) technique is used to identify distinct sign languages. To improve the results of the dilated ConvLSTM model, the hyperparameter selection process is executed using the EDMO model. To illustrate the significance of the EDMODL-ASLD technique, a brief experimental validation is made on the Arabic SLR database. The experimental validation of the EDMODL-ASLD technique portrayed a superior accuracy value of 96.47% over recent DL approaches.

Dynamic Analysis and FPGA Implementation of Fractional-Order Hopfield Networks with Memristive Synapse

Memristors have become important components in artificial synapses due to their ability to emulate the information transmission and memory functions of biological synapses. Unlike their biological counterparts, which adjust synaptic weights, memristor-based artificial synapses operate by altering conductance or resistance, making them useful for enhancing the processing capacity and storage capabilities of neural networks. When integrated into systems like Hopfield neural networks, memristors enable the study of complex dynamic behaviors, such as chaos and multistability. Moreover, fractional calculus is significant for their ability to model memory effects, enabling more accurate simulations of complex systems. Fractional-order Hopfield networks, in particular, exhibit chaotic and multistable behaviors not found in integer-order models. By combining memristors with fractional-order Hopfield neural networks, these systems offer the possibility of investigating different dynamic phenomena in artificial neural networks. This study investigates the dynamical behavior of a fractional-order Hopfield neural network (HNN) incorporating a memristor with a piecewise segment function in one of its synapses, highlighting the impact of fractional-order derivatives and memristive synapses on the stability, robustness, and dynamic complexity of the system. Using a network of four neurons as a case study, it is demonstrated that the memristive fractional-order HNN exhibits multistability, coexisting chaotic attractors, and coexisting limit cycles. Through spectral entropy analysis, the regions in the initial condition space that display varying degrees of complexity are mapped, highlighting those areas where the chaotic series approach a pseudo-random sequence of numbers. Finally, the proposed fractional-order memristive HNN is implemented on a Field-Programmable Gate Array (FPGA), demonstrating the feasibility of real-time hardware realization.

Monte Carlo Simulation Via Visual Basic Application

Objective: The objective of this study is to implement the Monte Carlo Simulation method using Microsoft Excel's Visual Basic for Applications (VBA). This approach enables users to simulate complex systems easily and obtain statistical outputs for decision-making. Theoretical Framework: Implementing the results of the changes we desire on a real-life system through trial and error can lead to costly and risky outcomes. Simulation allows us to develop a computer model of an existing system, enabling us to understand the system's behavior. Method: Monte Carlo Simulation, a widely used simulation technique, utilizes random sampling to estimate possible outcomes of a process or experiment. Monte Carlo Simulation utilizes random sampling to estimate the outcomes of an experiment. Results and Discussion: The purpose of this study is to implement the Monte Carlo Simulation method using MS Excel Visual Basic Application. When the user interacts with the Excel interface by clicking the "Calculate" button, the system generates random numbers based on predefined probability distributions. Subsequently, statistical measures such as the mean, variance, standard deviation, median, mode, range, and extreme values (maximum and minimum) are calculated using VBA. Research Implications: Thanks to the developed application, Monte Carlo Simulation applications can be calculated easily. Originality/Value: This study contributes to the literature by implementing the Monte Carlo Simulation method using Microsoft Excel's Visual Basic for Applications (VBA). This approach enables users to simulate complex systems easily and obtain statistical outputs for decision-making. These statistics provide insights into the underlying randomness and variability of the system, which aids in decision-making processes.

Constrained nuclear-electronic orbital QM/MM approach for simulating complex systems with quantum nuclear delocalization effects incorporated

The hybrid quantum mechanics/molecular mechanics (QM/MM) approach, which combines the accuracy of QM methods with the efficiency of MM methods, is widely used in the study of complex systems. However, past QM/MM implementations often neglect or face challenges in addressing nuclear quantum effects, despite their crucial role in many key chemical and biological processes. Recently, our group developed the constrained nuclear-electronic orbital (CNEO) theory, a cost-efficient approach that accurately addresses nuclear quantum effects, especially quantum nuclear delocalization effects. In this work, we integrate CNEO with the QM/MM approach through the electrostatic embedding scheme and apply the resulting CNEO QM/MM to two hydrogen-bonded complexes. We find that both solvation effects and nuclear quantum effects significantly impact hydrogen bond structures and dynamics. Notably, in the glutamic acid–glutamate complex, which mimics a common low barrier hydrogen bond in biological systems, CNEO QM/MM accurately predicts nearly equal proton sharing between the two residues. With an accurate description of both quantum nuclear delocalization effects and environmental effects, CNEO QM/MM is a promising new approach for simulating complex chemical and biological systems.

EP.03A.01 Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, Statistics and Simulation of Alive Supersystems

EP.03A.01 Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, Statistics and Simulation of Alive Supersystems

A heterogeneous model quantization and similarity matching approach based on structural features for MBSE

With the development of Model-Based Systems Engineering (MBSE), model reuse technology is an important method to improve the modeling efficiency and credibility, and the research difficulties and hotspots in the field of complex system simulation. However, model reuse still faces challenges due to the existence of text structure bias and feature mismatch. To this end, this paper presents a structure-based model similarity quantization algorithm to obtain the similarities among models. In the first stage, the properties of the structure between the models are obtained by the structural vectorization method, and different module data are accounted. In the second stage, weights are assigned to the module data using the AHP–CRITIC assignment method. The experiment is based on the circuit amplifier model as a case study, and the experimental results verify the feasibility of structure-based model similarity quantification.

Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm

In this paper, a new method is designed to effectively determine the parameters of proton exchange membrane fuel cells (PEMFCs), i.e., ξ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\xi }_{1}$$\\end{document}, ξ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\xi }_{2}$$\\end{document}, ξ3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\xi }_{3}$$\\end{document}, ξ4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\xi }_{4}$$\\end{document}, RC\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{\ ext{C}}$$\\end{document}, λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}, and b\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$b$$\\end{document}. The fuel cells (FCs) involve multiple variable quantities with complex non-linear behaviours, demanding accurate modelling to ensure optimal operation. An accurate model of these FCs is essential to evaluate their performance accurately. Furthermore, the design of the FCs significantly impacts simulation studies, which are crucial for various technological applications. This study proposed an improved parameter estimation procedure for PEMFCs by using the GOOSE algorithm, which was inspired by the adaptive behaviours found in geese during their relaxing and foraging times. The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. In order to validate the proposed algorithm, a number of experiments using various datasets were conducted and compared the outcomes with different state-of-the-art algorithms. The outcomes indicate that the proposed GOOSE algorithm not only produced promising results but also exhibited superior performance in comparison to other similar algorithms. This approach demonstrates the ability of the GOOSE algorithm to simulate complex systems and enhances the robustness and adaptability of the simulation tool by integrating essential behaviours into the computational framework. The proposed strategy facilitates the development of more accurate and effective advancements in the utilization of FCs.

(Invited) On the Definition of Velocity in Statistically Accurate, Discrete-Time Simulations of Thermodynamics

One of the most basic struggles in simulations of statistical and dynamical systems is how to appropriately balance the desire for simulation accuracy, obtained in the small time step limit, with simulation efficiency, obtained for large time steps. Obviously, we would like to have the benefits of both limits of accuracy and efficiency. Thus, understanding the influence of discrete time on the behavior of equations of motion is crucial for the understanding and optimization of numerical simulations in physical science and engineering. There are two different types of crucial discretization errors. One is that the simulated, discrete-time trajectory increasingly deviates from the true, continuous-time trajectory as the time step is increased. Another is that the simulated discrete-time velocity increasingly deviates from the expected velocity of the corresponding simulated trajectory as the time step is increased. While the former error is a signature that the simulated trajectory is not exactly the intended one, the simulation may still be representative of the true system. In contrast, the latter error is a signature of a fundamental internal inconsistency in the simulation, which may greatly complicate physical interpretation of the simulation results, especially when both configurational and kinetic measures are required. It is therefore crucially important to understand the relationship between a discrete-time coordinate and its corresponding velocity.It has previously been shown that, unlike in continuous-time, discrete-time configurational dynamics does not need a discrete-time velocity variable. This realization allowed for the derivation of the complete configurational GJ set of stochastic Verlet-type algorithms for simulating the Langevin equation. This set has the key feature of simultaneously giving exact, time-step independent statistical sampling of damped, noisy harmonic oscillators, and giving exact spatial transport values, drift and Einstein diffusion, on flat surfaces. The apparent fact that a discrete-time velocity is not required to simulate a coordinate trajectory implies that discrete-time velocity is an ambiguous concept, which, in turn, implies that multiple velocity definitions can be entertained in order to avoid internal simulation inconsistencies between configurational and kinetic measures. From the derivation of the stochastic GJ methods it was shown that a statistically correct velocity obtained at the same time as the coordinate (an on-site velocity) cannot be obtained. In contrast, a velocity at the half-step between consecutive configurational time steps (a half-step velocity) was identified to produce exact statistical kinetic response for harmonic oscillators regardless of the applied time step. However, only for a single GJ method does this velocity also give the correct drift value.We here derive new, practical velocity definitions that are optimized for statistical kinetic measures, such that, e.g., measures of kinetic energy and correlations with the configurational coordinate are exact for the noisy harmonic oscillator, while also producing the correct kinetic measure of transport on flat surfaces. We identify and analyze three half-step velocity definitions that fulfill these criteria, and we outline simple and practical Verlet-type algorithms that incorporate these velocities within the statistically desirable GJ format that provides the optimized configurational statistics. The application of the methods are demonstrated through characteristic Molecular Dynamics simulations of both simple and complex systems, illustrating how the resulting methods are able to efficiently and simultaneously simulate configurational and kinetic statistical ensembles in different situations using very large time steps.

Digital Implementation of Chaotic Systems UsingNvidia Jetson AGX Orin and Custom DAC Converter

Digital implementation of chaotic systems has numerous advantages, offering higher flexibility and computational accuracy compared to traditional analog systems. This is particularly important for engineering applications. Digital solutions are also more cost-effective. In this study, the Nvidia Jetson AGX Orin board and a 16-bit converter board are used for the digital realization of chaotic systems. The Nvidia Jetson AGX Orin provides high processing power and energy efficiency, while the 16-bit converter card ensures precise data processing. This setup allows for advanced, flexible, reliable, and economical solutions, enhancing various engineering applications, such as modeling, simulation, and control of complex systems. Digital platforms also facilitate parametric investigations and optimization processes, enabling more in-depth analysis.

Advanced Computational Methods for Modeling, Prediction and Optimization-A Review.

This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical engineering, and energy systems. We identified key trends and highlighted the integration of artificial intelligence (AI) with traditional computational methods. Some of the cited works were previously published within the topic: "Computational Methods: Modeling, Simulations, and Optimization of Complex Systems"; thus, this article compiles the latest reports from this field. The work presents various contemporary applications of advanced computational algorithms, including AI methods. It also introduces proposals for novel strategies in materials production and optimization methods within the energy systems domain. It is essential to optimize the properties of materials used in energy. Our findings demonstrate significant improvements in accuracy and efficiency, offering valuable insights for researchers and practitioners. This review contributes to the field by synthesizing state-of-the-art developments and suggesting directions for future research, underscoring the critical role of these methods in advancing engineering and technological solutions.

Domain Decomposition and Model Order Reduction for Electromagnetic Field Simulations in Carbon Fiber Composite Materials

The computation of the electric field in composite materials at the microscopic scale results in an immense number of degrees of freedom. Consequently, this often leads to prohibitively long computation times and extensive memory requirements, making direct computation impractical. In this study, one employs an innovative approach that integrates domain decomposition and model order reduction to retain local information while significantly reducing computation time. Domain decomposition allows for the division of the computational domain into smaller, more manageable subdomains, enabling parallel processing and reducing the overall complexity of the problem. Model order reduction further enhances this by approximating the solution in a lower-dimensional subspace, thereby minimising the number of unknown variables that need to be computed. Comparative analysis between the results obtained from the reduced model and those from direct resolution demonstrates that our method not only reduces computation time but also maintains accuracy. The new method effectively captures the essential characteristics of the electric field distribution in composite materials, ensuring that the local phenomena are accurately represented. This study provides a contribution to the field of computational electromagnetics by presenting a feasible solution to the challenges posed by the high computational demands of simulating composite materials at the microscopic scale. The proposed methodology offers a promising direction for future research and practical applications, enabling more efficient and accurate simulations of complex material systems.

System dynamics-multiple the objective optimization model for the coordinated development of urban economy-energy-carbon system

The deepening conflict between human societal progress and efforts to combat climate change necessitates immediate coordination among the Economy-Energy-Carbon (EEC) management. The study applies IPCC accounting methods to compute the historical carbon emissions in the Chengdu-Chongqing economic circle from 2005 to 2021. It assesses the developmental efficiency of various subsystems in the EEC system by employing the rank-sum ratio. Taking a typical city in the region (Chongqing) as a case study, this study integrates System Dynamics (SD) with NSGAIII to develop an EEC multi-objective optimization model. It utilizes the VIKOR model to select the optimal solution. Based on this, the analysis explores the coupled and coordinated state of the EEC system from 2022 to 2030 under the optimal solution mode. Research findings indicate that: (1) The overall carbon emissions exhibit a slight increase, where carbon sources surpass the carbon sink created by land use. (2) Compared to results from other preference-based scenarios, in the scenario where subsystem efficiencies are all optimal, the minimum cumulative carbon emissions amount to 1520.52 million tons, with the economy maintaining a favorable status. Moreover, the overall coupling coordination of the EEC is in a relatively favorable state, and it tends to improve annually. (3) To attain the coordinated development of the EEC system by 2030, the energy structure of the representative city must decrease coal consumption by 26.17%, maintain the construction land ratio below 3.7%, and guarantee forest coverage exceeds 57.77%. This study, utilizing complex system simulations and optimization analyses across various scenarios, offers pertinent recommendations for attaining urban low-carbon development while upholding societal advancement.

A revised cognitive mapping methodology for modeling and simulation

Fuzzy Cognitive Maps (FCMs) hold promise as a mathematical tool for modeling and simulating complex systems due to their transparency, flexibility to operate on prior knowledge structures and recurrent reasoning characteristics. However, they suffer from significant shortcomings that have prevented them from being more widely used. Some of these issues include discrepancies in component interpretation, saturation of neural concepts, arbitrary nonlinearities, and dynamic behaviors that are difficult to align with the problem domain. By integrating theoretical advances with practical needs, this paper proposes a revised modeling and simulation methodology termed “neural cognitive mapping” that addresses these issues holistically. Firstly, we redefine concepts’ activation values in terms of changes rather than absolute values, ensuring a unified interpretation of the model’s components. Secondly, we propose a parameterized activation function, called “exponential normalized activator”, which allows experts to control the neurons’ nonlinearities while avoiding saturation states. Furthermore, we provide a twofold reasoning rule that simultaneously computes the concepts’ changes and the amounts of resources attached to problem variables. Thirdly, we introduce a framework for interpreting simulation results across various dynamic behaviors, including scenarios with unique fixed-point attractors. The simulations using both real-world case studies and synthetically generated data illustrate the superiority of our proposal compared with the traditional approach in terms of clarity, usefulness, consistency, and controllability. Moreover, the empirical studies opened new research directions to be explored in future research.

Using cellular automata and multi-criteria evaluation to simulate the wildfire expansion in Prince George, British Co-lumbia

Wildfires pose a critical and ongoing challenge in British Columbia, Canada, threatening human life, property, and natural ecosystems. To understand and predict the behavior of such fires, our study employs Cellular Automata (CA), a mathematical model adept at simulating complex systems through grid-based cell interactions. This model, validated by prior research, incorporates a wind propagation rule that significantly enhances the prediction of wildfire spread in the direction of prevailing winds. Research centers on a wildfire event in Prince George, utilizing CA to simulate fire dynamics influenced by var-ious factors. The models strength lies in its ability to represent detailed local interactions and its flexibility in scenario testing, which is instrumental in understanding model uncer-tainties. By simulating different fire scenarios, the study aims to grasp the complexities and potential variables affecting wildfire behavior. The research provides a foundation for decision-makers to analyze and study wildfire events, leveraging a Multi-Criteria Evaluation (MCE) Model to assess the susceptibility of cells to fire. This comprehensive approach combines CA with MCE, offering a robust framework for simulating and manag-ing wildfire expansion in British Columbia.

Assessment of Embedding Schemes in a Hybrid Machine Learning/Classical Potentials (ML/MM) Approach.

Machine learning (ML) methods have reached high accuracy levels for the prediction of in vacuo molecular properties. However, the simulation of large systems solely through ML methods (such as those based on neural network potentials) is still a challenge. In this context, one of the most promising frameworks for integrating ML schemes in the simulation of complex molecular systems are the so-called ML/MM methods. These multiscale approaches combine ML methods with classical force fields (MM), in the same spirit as the successful hybrid quantum mechanics-molecular mechanics methods (QM/MM). The key issue for such ML/MM methods is an adequate description of the coupling between the region of the system described by ML and the region described at the MM level. In the context of QM/MM schemes, the main ingredient of the interaction is electrostatic, and the state of the art is the so-called electrostatic-embedding. In this study, we analyze the quality of simpler mechanical embedding-based approaches, specifically focusing on their application within a ML/MM framework utilizing atomic partial charges derived in vacuo. Taking as reference electrostatic embedding calculations performed at a QM(DFT)/MM level, we explore different atomic charges schemes, as well as a polarization correction computed using atomic polarizabilites. Our benchmark data set comprises a set of about 80k small organic structures from the ANI-1x and ANI-2x databases, solvated in water. The results suggest that the minimal basis iterative stockholder (MBIS) atomic charges yield the best agreement with the reference coupling energy. Remarkable enhancements are achieved by including a simple polarization correction.

Efficient learning of accurate surrogates for simulations of complex systems

Efficient learning of accurate surrogates for simulations of complex systems