Journal articles: 'Computational methods for Complex Systems'

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Jansen, Thomas L. C. "Computational spectroscopy of complex systems." Journal of Chemical Physics 155, no. 17 (November 7, 2021): 170901. http://dx.doi.org/10.1063/5.0064092.

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Numerous linear and non-linear spectroscopic techniques have been developed to elucidate structural and functional information of complex systems ranging from natural systems, such as proteins and light-harvesting systems, to synthetic systems, such as solar cell materials and light-emitting diodes. The obtained experimental data can be challenging to interpret due to the complexity and potential overlapping spectral signatures. Therefore, computational spectroscopy plays a crucial role in the interpretation and understanding of spectral observables of complex systems. Computational modeling of various spectroscopic techniques has seen significant developments in the past decade, when it comes to the systems that can be addressed, the size and complexity of the sample types, the accuracy of the methods, and the spectroscopic techniques that can be addressed. In this Perspective, I will review the computational spectroscopy methods that have been developed and applied for infrared and visible spectroscopies in the condensed phase. I will discuss some of the questions that this has allowed answering. Finally, I will discuss current and future challenges and how these may be addressed.

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Arora, J. S., and P. B. Thanedar. "Computational methods for optimum design of large complex systems." Computational Mechanics 1, no. 3 (1986): 221–42. http://dx.doi.org/10.1007/bf00272625.

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Udwadia, Firdaus E., and Nami Mogharabin. "New Directions in Modeling and Computational Methods for Complex Mechanical Dynamical Systems." Processes 10, no. 8 (August 9, 2022): 1560. http://dx.doi.org/10.3390/pr10081560.

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This paper presents a new conceptualization of complex nonlinear mechanical systems and develops new and novel computational methods for determining their response to given applied forces and torques. The new conceptualization uses the idea of including particles of zero mass to describe the dynamics of such systems. This leads to simplifications in the development of their equations of motion and engenders a straightforward new computational approach to simulate their behavior. The purpose of the paper is to develop a new analytical and computational methodology to handle complex systems and to illustrate it through the study of an old unsolved problem in classical mechanics, that of a non-uniform rigid spherical shell rolling, without slipping, under gravity on an arbitrary dimpled bowl-shaped rigid surface. The new conceptualization provides the explicit equations of motion for the system, the analytical determination of the reaction forces supplied by the surface, and a straightforward computational approach to simulate the dynamics. Detailed analytical and numerical results are provided. The computations illustrate the complexity of the dynamical behavior of the system and its high sensitivity to the initial orientation of the shell and to the presence of any initial angular velocity normal to the surface.

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Villaverde, Alejandro F., Carlo Cosentino, Attila Gábor, and Gábor Szederkényi. "Computational Methods for Identification and Modelling of Complex Biological Systems." Complexity 2019 (April 7, 2019): 1–3. http://dx.doi.org/10.1155/2019/4951650.

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Gu, Kai Liu and Guiding. "Improved PMHSS Iteration Methods for Complex Symmetric Linear Systems." Journal of Computational Mathematics 37, no. 2 (June 2019): 278–96. http://dx.doi.org/10.4208/jcm.1702-m2017-0007.

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Sosnowski, Marcin, Jaroslaw Krzywanski, and Radomír Ščurek. "Artificial Intelligence and Computational Methods in the Modeling of Complex Systems." Entropy 23, no. 5 (May 10, 2021): 586. http://dx.doi.org/10.3390/e23050586.

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Tavassoly, Iman, Joseph Goldfarb, and Ravi Iyengar. "Systems biology primer: the basic methods and approaches." Essays in Biochemistry 62, no. 4 (October 4, 2018): 487–500. http://dx.doi.org/10.1042/ebc20180003.

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Systems biology is an integrative discipline connecting the molecular components within a single biological scale and also among different scales (e.g. cells, tissues and organ systems) to physiological functions and organismal phenotypes through quantitative reasoning, computational models and high-throughput experimental technologies. Systems biology uses a wide range of quantitative experimental and computational methodologies to decode information flow from genes, proteins and other subcellular components of signaling, regulatory and functional pathways to control cell, tissue, organ and organismal level functions. The computational methods used in systems biology provide systems-level insights to understand interactions and dynamics at various scales, within cells, tissues, organs and organisms. In recent years, the systems biology framework has enabled research in quantitative and systems pharmacology and precision medicine for complex diseases. Here, we present a brief overview of current experimental and computational methods used in systems biology.

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Locurcio, Marco, Francesco Tajani, and Pierluigi Morano. "Computational Methods Applied to Data Analysis for Modeling Complex Real Estate Systems." Complexity 2020 (July 9, 2020): 1–3. http://dx.doi.org/10.1155/2020/8519060.

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Puzynin, I. V., T. L. Boyadzhiev, S. I. Vinitskii, E. V. Zemlyanaya, T. P. Puzynina, and O. Chuluunbaatar. "Methods of computational physics for investigation of models of complex physical systems." Physics of Particles and Nuclei 38, no. 1 (February 2007): 70–116. http://dx.doi.org/10.1134/s1063779607010030.

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Puzyrkov, Dmitry, Sergey Polyakov, Viktoriia Podryga, and Sergey Markizov. "Concept of a Cloud Service for Data Preparation and Computational Control on Custom HPC Systems in Application to Molecular Dynamics." EPJ Web of Conferences 173 (2018): 05014. http://dx.doi.org/10.1051/epjconf/201817305014.

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At the present stage of computer technology development it is possible to study the properties and processes in complex systems at molecular and even atomic levels, for example, by means of molecular dynamics methods. The most interesting are problems related with the study of complex processes under real physical conditions. Solving such problems requires the use of high performance computing systems of various types, for example, GRID systems and HPC clusters. Considering the time consuming computational tasks, the need arises of software for automatic and unified monitoring of such computations. A complex computational task can be performed over different HPC systems. It requires output data synchronization between the storage chosen by a scientist and the HPC system used for computations. The design of the computational domain is also quite a problem. It requires complex software tools and algorithms for proper atomistic data generation on HPC systems. The paper describes the prototype of a cloud service, intended for design of atomistic systems of large volume for further detailed molecular dynamic calculations and computational management for this calculations, and presents the part of its concept aimed at initial data generation on the HPC systems.

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Xiao, Xiao-Yong, and Hong-Wei Yin. "Efficient parameterized HSS iteration methods for complex symmetric linear systems." Computers & Mathematics with Applications 73, no. 1 (January 2017): 87–95. http://dx.doi.org/10.1016/j.camwa.2016.10.022.

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Fearnhead, Paul. "Computational methods for complex stochastic systems: a review of some alternatives to MCMC." Statistics and Computing 18, no. 2 (November 29, 2007): 151–71. http://dx.doi.org/10.1007/s11222-007-9045-8.

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Park, Buhm Soon. "Between Accuracy and Manageability: Computational Imperatives in Quantum Chemistry." Historical Studies in the Natural Sciences 39, no. 1 (2009): 32–62. http://dx.doi.org/10.1525/hsns.2009.39.1.32.

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This article explores the place of computation in the history of quantum theory by examining the development of several approximation methods to solve the Schröödinger equation without using empirical information, as these were worked out in the years from 1927 to 1933. These ab initio methods, as they became known, produced the results that helped validate the use of quantum mechanics in many-body atomic and molecular systems, but carrying out the computations became increasingly laborious and difficult as better agreement between theory and experiment was pursued and more complex systems were tackled. I argue that computational work in the early years of quantum chemistry shows an emerging practice of theory that required human labor, technological improvement (computers), and mathematical ingenuity.

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Xiao, Xiao-Yong, Xiang Wang, and Hong-Wei Yin. "Efficient preconditioned NHSS iteration methods for solving complex symmetric linear systems." Computers & Mathematics with Applications 75, no. 1 (January 2018): 235–47. http://dx.doi.org/10.1016/j.camwa.2017.09.004.

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Tiwary, Basant K. "Computational medicine: quantitative modeling of complex diseases." Briefings in Bioinformatics 21, no. 2 (January 30, 2019): 429–40. http://dx.doi.org/10.1093/bib/bbz005.

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Abstract Biological complex systems are composed of numerous components that interact within and across different scales. The ever-increasing generation of high-throughput biomedical data has given us an opportunity to develop a quantitative model of nonlinear biological systems having implications in health and diseases. Multidimensional molecular data can be modeled using various statistical methods at different scales of biological organization, such as genome, transcriptome and proteome. I will discuss recent advances in the application of computational medicine in complex diseases such as network-based studies, genome-scale metabolic modeling, kinetic modeling and support vector machines with specific examples in the field of cancer, psychiatric disorders and type 2 diabetes. The recent advances in translating these computational models in diagnosis and identification of drug targets of complex diseases are discussed, as well as the challenges researchers and clinicians are facing in taking computational medicine from the bench to bedside.

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Coelho, João Paulo, and José Boaventura-Cunha. "Long Term Solar Radiation Forecast Using Computational Intelligence Methods." Applied Computational Intelligence and Soft Computing 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/729316.

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The point prediction quality is closely related to the model that explains the dynamic of the observed process. Sometimes the model can be obtained by simple algebraic equations but, in the majority of the physical systems, the relevant reality is too hard to model with simple ordinary differential or difference equations. This is the case of systems with nonlinear or nonstationary behaviour which require more complex models. The discrete time-series problem, obtained by sampling the solar radiation, can be framed in this type of situation. By observing the collected data it is possible to distinguish multiple regimes. Additionally, due to atmospheric disturbances such as clouds, the temporal structure between samples is complex and is best described by nonlinear models. This paper reports the solar radiation prediction by using hybrid model that combines support vector regression paradigm and Markov chains. The hybrid model performance is compared with the one obtained by using other methods like autoregressive (AR) filters, Markov AR models, and artificial neural networks. The results obtained suggests an increasing prediction performance of the hybrid model regarding both the prediction error and dynamic behaviour.

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MARKHAM, G. "Conjugate Gradient Type Methods for Indefinite, Asymmetric, and Complex Systems." IMA Journal of Numerical Analysis 10, no. 2 (1990): 155–70. http://dx.doi.org/10.1093/imanum/10.2.155.

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Alexandrov, Dmitri V., and Andrey Yu Zubarev. "Transport phenomena in complex systems (part 1)." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2205 (July 19, 2021): 20200301. http://dx.doi.org/10.1098/rsta.2020.0301.

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The issue, in two parts, is devoted to theoretical, computational and experimental studies of transport phenomena in various complex systems (in porous and composite media; systems with physical and chemical reactions and phase and structural transformations; in biological tissues and materials). Various types of these phenomena (heat and mass transfer; hydrodynamic and rheological effects; electromagnetic field propagation) are considered. Anomalous, relaxation and nonlinear transport, as well as transport induced by the impact of external fields and noise, is the focus of this issue. Modern methods of computational modelling, statistical physics and hydrodynamics, nonlinear dynamics and experimental methods are presented and discussed. Special attention is paid to transport phenomena in biological systems (such as haemodynamics in stenosed and thrombosed blood vessels magneto-induced heat generation and propagation in biological tissues, and anomalous transport in living cells) and to the development of a scientific background for progressive methods in cancer, heart attack and insult therapy (magnetic hyperthermia for cancer therapy, magnetically induced circulation flow in thrombosed blood vessels and non-contact determination of the local rate of blood flow in coronary arteries). The present issue includes works on the phenomenological study of transport processes, the derivation of a macroscopic governing equation on the basis of the analysis of a complicated internal reaction and the microscopic determination of macroscopic characteristics of the studied systems. This article is part of the theme issue ‘Transport phenomena in complex systems (part 1)’.

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Altherr, Lena C., Laura Joggerst, Philipp Leise, Marc E. Pfetsch, Andreas Schmitt, and Janine Wendt. "On Obligations in the Development Process of Resilient Systems with Algorithmic Design Methods." Applied Mechanics and Materials 885 (November 2018): 240–52. http://dx.doi.org/10.4028/www.scientific.net/amm.885.240.

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Advanced computational methods are needed both for the design of large systems and to compute high accuracy solutions. Such methods are efficient in computation, but the validation of results is very complex, and highly skilled auditors are needed to verify them. We investigate legal questions concerning obligations in the development phase, especially for technical systems developed using advanced methods. In particular, we consider methods of resilient and robust optimization. With these techniques, high performance solutions can be found, despite a high variety of input parameters. However, given the novelty of these methods, it is uncertain whether legal obligations are being met. The aim of this paper is to discuss if and how the choice of a specific computational method affects the developer’s product liability. The review of legal obligations in this paper is based on German law and focuses on the requirements that must be met during the design and development process.

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Cheung, Charles, Marianna Safronova, and Sergey Porsev. "Scalable Codes for Precision Calculations of Properties of Complex Atomic Systems." Symmetry 13, no. 4 (April 8, 2021): 621. http://dx.doi.org/10.3390/sym13040621.

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High precision atomic data are indispensable for studies of fundamental symmetries, tests of fundamental physics postulates, developments of atomic clocks, ultracold atom experiments, astrophysics, plasma science, and many other fields of research. We have developed a new parallel atomic structure code package that enables computations that were not previously possible due to system complexity. This code package also allows much quicker computations to be run with higher accuracy for simple systems. We explored different methods of load-balancing matrix element calculations for many-electron systems, which are very difficult due to the intrinsic nature of the computational methods used to calculate them. Furthermore, dynamic memory allocation and MPI parallelization have been implemented to optimize and accelerate the computations. We have achieved near-perfect linear scalability and efficiency with the number of processors used for calculation, paving the way towards the future where most open-shell systems will finally be able to be treated with good accuracy. We present several examples illustrating new capabilities of the newly developed codes, specifically correlating up to all 60 electrons in the highly charged Ir17+ ion and predicting certain properties of Fe16+.

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Yatskou, M. M., and V. V. Apanasovich. "Data analysis in complex biomolecular systems." Informatics 18, no. 1 (March 29, 2021): 105–22. http://dx.doi.org/10.37661/1816-0301-2021-18-1-105-122.

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The biomolecular technology progress is directly related to the development of effective methods and algorithms for processing a large amount of information obtained by modern high-throughput experimental equipment. The priority task is the development of promising computational tools for the analysis and interpretation of biophysical information using the methods of big data and computer models. An integrated approach to processing large datasets, which is based on the methods of data analysis and simulation modelling, is proposed. This approach allows to determine the parameters of biophysical and optical processes occurring in complex biomolecular systems. The idea of an integrated approach is to use simulation modelling of biophysical processes occurring in the object of study, comparing simulated and most relevant experimental data selected by dimension reduction methods, determining the characteristics of the investigated processes using data analysis algorithms. The application of the developed approach to the study of bimolecular systems in fluorescence spectroscopy experiments is considered. The effectiveness of the algorithms of the approach was verified by analyzing of simulated and experimental data representing the systems of molecules and proteins. The use of complex analysis increases the efficiency of the study of biophysical systems during the analysis of big data.

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Needleman, A. "Computational Mechanics." Applied Mechanics Reviews 38, no. 10 (October 1, 1985): 1282–83. http://dx.doi.org/10.1115/1.3143692.

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Computational methods play a key role in solid mechanics, as a way of modelling fundamental aspects of mechanical behavior, as a vehicle for transferring this improved modelling capability into new engineering tools, and as a means of utilizing these tools in engineering practice. Modern computational methods enable realistic models of mechanical systems to be formulated without regard as to whether or not analytical solutions are feasible. Increased computational capability is also an incentive for developing more accurate theories, since it becomes possible to use such theories to solve complex engineering problems.

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Paquet, Eric, and Herna L. Viktor. "Computational Methods for Ab Initio Molecular Dynamics." Advances in Chemistry 2018 (April 29, 2018): 1–14. http://dx.doi.org/10.1155/2018/9839641.

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Ab initio molecular dynamics is an irreplaceable technique for the realistic simulation of complex molecular systems and processes from first principles. This paper proposes a comprehensive and self-contained review of ab initio molecular dynamics from a computational perspective and from first principles. Quantum mechanics is presented from a molecular dynamics perspective. Various approximations and formulations are proposed, including the Ehrenfest, Born–Oppenheimer, and Hartree–Fock molecular dynamics. Subsequently, the Kohn–Sham formulation of molecular dynamics is introduced as well as the afferent concept of density functional. As a result, Car–Parrinello molecular dynamics is discussed, together with its extension to isothermal and isobaric processes. Car–Parrinello molecular dynamics is then reformulated in terms of path integrals. Finally, some implementation issues are analysed, namely, the pseudopotential, the orbital functional basis, and hybrid molecular dynamics.

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Xiao, Xiao-Yong, Xiang Wang, and Hong-Wei Yin. "Efficient single-step preconditioned HSS iteration methods for complex symmetric linear systems." Computers & Mathematics with Applications 74, no. 10 (November 2017): 2269–80. http://dx.doi.org/10.1016/j.camwa.2017.07.007.

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Zhang, Meixiang, Zhi Zhang, Satya Chan, and Sooyoung Kim. "Computationally Efficient Soft Detection Schemes for Coded Massive MIMO Systems." Electronics 9, no. 2 (February 17, 2020): 344. http://dx.doi.org/10.3390/electronics9020344.

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This paper presents a computationally efficient soft detection scheme for massive multiple-input multiple-output (MIMO) systems. The proposed scheme adopts joint iterative detection and decoding (JIDD) methods for their capacity limiting performances. In addition, the minimum mean square error parallel interference cancellation (MMSE-PIC)-based detection scheme is used for soft information exchange. We propose a number of techniques to reduce the computational complexity, while keeping almost the same performance as the conventional ones. First, a technique is proposed to approximate the Gram matrix to a constant valued diagonal matrix. This proposal can lead to elimination of complex matrix inversion process and multiple layer dependent estimations, resulting in huge complexity reduction. Second, compact equations to estimate soft-symbol values for M-ary (quadrature amplitude modulation) QAM are derived. From the investigation example of 2 8 -QAM in this paper, this proposal showed more than two orders of less computations compared to the conventional scheme. The simulation results demonstrate that the proposed method can achieve approximating performance to the conventional method with a largely reduced computational complexity.

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Cioffi, Valeria, Lucia Luciana Mosca, Enrico Moretto, Ottavio Ragozzino, Roberta Stanzione, Mario Bottone, Nelson Mauro Maldonato, Benedetta Muzii, and Raffaele Sperandeo. "Computational Methods in Psychotherapy: A Scoping Review." International Journal of Environmental Research and Public Health 19, no. 19 (September 28, 2022): 12358. http://dx.doi.org/10.3390/ijerph191912358.

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Background: The study of complex systems, such as the psychotherapeutic encounter, transcends the mechanistic and reductionist methods for describing linear processes and needs suitable approaches to describe probabilistic and scarcely predictable phenomena. Objective: The present study undertakes a scoping review of research on the computational methods in psychotherapy to gather new developments in this field and to better understand the phenomena occurring in psychotherapeutic interactions as well as in human interaction more generally. Design: Online databases were used to identify papers published 2011–2022, from which we selected 18 publications from different resources, selected according to criteria established in advance and described in the text. A flow chart and a summary table of the articles consulted have been created. Results: The majority of publications (44.4%) reported combined computational and experimental approaches, so we grouped the studies according to the types of computational methods used. All but one of the studies collected measured data. All the studies confirmed the usefulness of predictive and learning models in the study of complex variables such as those belonging to psychological, psychopathological and psychotherapeutic processes. Conclusions: Research on computational methods will benefit from a careful selection of reference methods and standards. Therefore, this review represents an attempt to systematise the empirical literature on the applications of computational methods in psychotherapy research in order to offer clinicians an overview of the usefulness of these methods and the possibilities of their use in the various fields of application, highlighting their clinical implications, and ultimately attempting to identify potential opportunities for further research.

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Bektas, Oguz, Jane Marshall, and Jeffrey A. Jones. "Comparison of Computational Prognostic Methods for Complex Systems Under Dynamic Regimes: A Review of Perspectives." Archives of Computational Methods in Engineering 27, no. 4 (May 7, 2019): 999–1011. http://dx.doi.org/10.1007/s11831-019-09339-7.

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Koblents, Eugenia, Inés P. Mariño, and Joaquín Míguez. "Bayesian Computation Methods for Inference in Stochastic Kinetic Models." Complexity 2019 (January 20, 2019): 1–15. http://dx.doi.org/10.1155/2019/7160934.

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In this paper we investigate Monte Carlo methods for the approximation of the posterior probability distributions in stochastic kinetic models (SKMs). SKMs are multivariate Markov jump processes that model the interactions among species in biological systems according to a set of usually unknown parameters. The tracking of the species populations together with the estimation of the interaction parameters is a Bayesian inference problem for which Markov chain Monte Carlo (MCMC) methods have been a typical computational tool. Specifically, the particle MCMC (pMCMC) method has been shown to be effective, while computationally demanding method applicable to this problem. Recently, it has been shown that an alternative approach to Bayesian computation, namely, the class of adaptive importance samplers, may be more efficient than classical MCMC-like schemes, at least for certain applications. For example, the nonlinear population Monte Carlo (NPMC) algorithm has yielded promising results with a low dimensional SKM (the classical predator-prey model). In this paper we explore the application of both pMCMC and NPMC to analyze complex autoregulatory feedback networks modelled by SKMs. We demonstrate numerically how the populations of the relevant species in the network can be tracked and their interaction rates estimated, even in scenarios with partial observations. NPMC schemes attain an appealing trade-off between accuracy and computational cost that can make them advantageous in many practical applications.

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Chen, Jih-Kuang. "Improved DEMATEL-ISM integration approach for complex systems." PLOS ONE 16, no. 7 (July 16, 2021): e0254694. http://dx.doi.org/10.1371/journal.pone.0254694.

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Purpose Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) are commonly used separately, but also may be combined per their common characteristics to identify causal relationships and hierarchical structure among factors in complex systems with a relatively small computational burden. The purpose of this study is to establish an improved DEMATEL-ISM integration approach to remedy the disadvantages of the traditional DEMATEL-ISM integration method. A case study was conducted to compare the proposed improved integration approach against the traditional integration method, and to validate its feasibility and effectiveness. Methods The proposed improved DEMATEL-ISM integration approach has two main parts: a threshold determination via maximum mean de-entropy (MMDE) method and an additional transitivity check process. The factors influencing China’s rural-urban floating population’s willingness to participate in social insurance was analyzed as a case study. Results The traditional and improved methods show notable differences in the hierarchical factor structure and the inner influence relationship among factors that they respectively reveal. The traditional integration approach results in some irrationality, while the improved approach does not. Originality This study confirms the importance of proper threshold determination and reachability matrix transitivity checking during DEMATEL-ISM integration. The improved approach includes a scientific threshold determination method based on the MMDE method, plus a transitivity check of the reachability matrix with necessary corrections to ensure its soundness. It can be straightforwardly operated at a relatively low computational burden while providing accurate analysis results.

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Ai, Jiali, Chi Zhai, and Wei Sun. "Study on the Formation of Complex Chemical Waveforms by Different Computational Methods." Processes 8, no. 4 (March 27, 2020): 393. http://dx.doi.org/10.3390/pr8040393.

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Chemical wave is a special phenomenon that presents periodic patterns in space-time domain, and the Belousov–Zhabotinsky (B-Z) reaction is the first well-known reaction-diffusion system that exhibits organized patterns out of a homogeneous environment. In this paper, the B-Z reaction kinetics is described by the Oregonator model, and formation and evolution of chemical waves are simulated based on this model. Two different simulation methods, partial differential equations (PDEs) and cellular automata (CA) are implemented to simulate the formation of chemical waveform patterns, i.e., target wave and spiral wave on a two-dimensional plane. For the PDEs method, reaction caused changes of molecules at different location are considered, as well as diffusion driven by local concentration difference. Specifically, a PDE model of the B-Z reaction is first established based on the B-Z reaction kinetics and mass transfer theory, and it is solved by a nine-point finite difference (FD) method to simulate the formation of chemical waves. The CA method is based on system theory, and interaction relations with the cells nearest neighbors are mainly concerned. By comparing these two different simulation strategies, mechanisms that cause the formation of complex chemical waves are explored, which provides a reference for the subsequent research on complex systems.

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Talaeizadeh, Amin, Mahmoodreza Forootan, Mehdi Zabihi, and Hossein Nejat Pishkenari. "Comparison of Kane’s and Lagrange’s Methods in Analysis of Constrained Dynamical Systems." Robotica 38, no. 12 (February 10, 2020): 2138–50. http://dx.doi.org/10.1017/s0263574719001899.

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SUMMARYDynamic modeling is a fundamental step in analyzing the movement of any mechanical system. Methods for dynamical modeling of constrained systems have been widely developed to improve the accuracy and minimize computational cost during simulations. The necessity to satisfy constraint equations as well as the equations of motion makes it more critical to use numerical techniques that are successful in decreasing the number of computational operations and numerical errors for complex dynamical systems. In this study, performance of a variant of Kane’s method compared to six different techniques based on the Lagrange’s equations is shown. To evaluate the performance of the mentioned methods, snake-like robot dynamics is considered and different aspects such as the number of the most time-consuming computational operations, constraint error, energy error, and CPU time assigned to each method are compared. The simulation results demonstrate the superiority of the variant of Kane’s method concerning the other ones.

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Agundis-Tinajero, Gibran, Rafael Peña Gallardo, Juan Segundo-Ramírez, Nancy Visairo-Cruz, and Josep M. Guerrero. "Performance assessment of shooting methods using parallel cloud computing." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 2 (March 4, 2019): 915–26. http://dx.doi.org/10.1108/compel-08-2018-0307.

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Purpose The purpose of this study is to present the performance evaluation of three shooting methods typically applied to obtain the periodic steady state of electric power systems, with the aim to check the benefits of the use of cloud computing regarding relative efficiency and computation time. Design/methodology/approach The mathematical formulation of the methods is presented, and their parallelization potential is explained. Two case studies are addressed, and the solution is computed with the shooting methods using multiple computer cores through cloud computing. Findings The results obtained show a reduction in the computation time and increase in the relative efficiency by the application of these methods with parallel cloud computing, in the problem of obtainment of the periodic steady state of electric power systems in an efficient way. Additionally, the characteristics of the methods, when parallel cloud computing is used, are shown and comparisons among them are presented. Originality/value The main advantage of employment of parallel cloud computing is a significant reduction of the computation time in the solution of the problem of a heavy computational load caused by the application of the shooting methods.

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Liu, Xiangrong, Zengyan Hong, Juan Liu, Yuan Lin, Alfonso Rodríguez-Patón, Quan Zou, and Xiangxiang Zeng. "Computational methods for identifying the critical nodes in biological networks." Briefings in Bioinformatics 21, no. 2 (February 12, 2019): 486–97. http://dx.doi.org/10.1093/bib/bbz011.

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Abstract A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost. Existing experimental methods in identifying critical nodes generally require much time, manpower and money. Accordingly, many scientists are attempting to solve this problem by researching efficient and low-cost computing methods. To facilitate calculations, biological networks are often modeled as several common networks. In this review, we classify biological networks according to the network types used by several kinds of common computational methods and introduce the computational methods used by each type of network.

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Li, Yongsheng, Xiyun Jin, Zishan Wang, Lili Li, Hong Chen, Xiaoyu Lin, Song Yi, Yunpeng Zhang, and Juan Xu. "Systematic review of computational methods for identifying miRNA-mediated RNA-RNA crosstalk." Briefings in Bioinformatics 20, no. 4 (October 25, 2017): 1193–204. http://dx.doi.org/10.1093/bib/bbx137.

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AbstractPosttranscriptional crosstalk and communication between RNAs yield large regulatory competing endogenous RNA (ceRNA) networks via shared microRNAs (miRNAs), as well as miRNA synergistic networks. The ceRNA crosstalk represents a novel layer of gene regulation that controls both physiological and pathological processes such as development and complex diseases. The rapidly expanding catalogue of ceRNA regulation has provided evidence for exploitation as a general model to predict the ceRNAs in silico. In this article, we first reviewed the current progress of RNA-RNA crosstalk in human complex diseases. Then, the widely used computational methods for modeling ceRNA-ceRNA interaction networks are further summarized into five types: two types of global ceRNA regulation prediction methods and three types of context-specific prediction methods, which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. To provide guidance in the computational prediction of ceRNA-ceRNA interactions, we finally performed a comparative study of different combinations of miRNA–target methods as well as five types of ceRNA identification methods by using literature-curated ceRNA regulation and gene perturbation. The results revealed that integration of different miRNA–target prediction methods and context-specific miRNA/gene expression profiles increased the performance for identifying ceRNA regulation. Moreover, different computational methods were complementary in identifying ceRNA regulation and captured different functional parts of similar pathways. We believe that the application of these computational techniques provides valuable functional insights into ceRNA regulation and is a crucial step for informing subsequent functional validation studies.

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Hulme, Adam, Scott Mclean, Paul M. Salmon, Jason Thompson, Ben R. Lane, and Rasmus Oestergaard Nielsen. "Computational methods to model complex systems in sports injury research: agent-based modelling (ABM) and systems dynamics (SD) modelling." British Journal of Sports Medicine 53, no. 24 (November 17, 2018): 1507–10. http://dx.doi.org/10.1136/bjsports-2018-100098.

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Andersen, Jakob L., Christoph Flamm, Daniel Merkle, and Peter F. Stadler. "An intermediate level of abstraction for computational systems chemistry." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 375, no. 2109 (November 13, 2017): 20160354. http://dx.doi.org/10.1098/rsta.2016.0354.

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Computational techniques are required for narrowing down the vast space of possibilities to plausible prebiotic scenarios, because precise information on the molecular composition, the dominant reaction chemistry and the conditions for that era are scarce. The exploration of large chemical reaction networks is a central aspect in this endeavour. While quantum chemical methods can accurately predict the structures and reactivities of small molecules, they are not efficient enough to cope with large-scale reaction systems. The formalization of chemical reactions as graph grammars provides a generative system, well grounded in category theory, at the right level of abstraction for the analysis of large and complex reaction networks. An extension of the basic formalism into the realm of integer hyperflows allows for the identification of complex reaction patterns, such as autocatalysis, in large reaction networks using optimization techniques. This article is part of the themed issue ‘Reconceptualizing the origins of life’.

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Kolumbet, Vadim, and Olha Svynchuk. "MULTIAGENT METHODS OF MANAGEMENT OF DISTRIBUTED COMPUTING IN HYBRID CLUSTERS." Advanced Information Systems 6, no. 1 (April 6, 2022): 32–36. http://dx.doi.org/10.20998/2522-9052.2022.1.05.

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Modern information technologies include the use of server systems, virtualization technologies, communication tools for distributed computing and development of software and hardware solutions of data processing and storage centers, the most effective of such complexes for managing heterogeneous computing resources are hybrid GRID- distributed computing infrastructure combines resources of different types with collective access to these resources for and sharing shared resources. The article considers a multi-agent system that provides integration of the computational management approach for a cluster Grid system of computational type, the nodes of which have a complex hybrid structure. The hybrid cluster includes computing modules that support different parallel programming technologies and differ in their computational characteristics. The novelty and practical significance of the methods and tools presented in the article are a significant increase in the functionality of the Grid cluster computing management system for the distribution and division of Grid resources at different levels of tasks, the ability to embed intelligent computing management tools in problem-oriented applications. The use of multi-agent systems for task planning in Grid systems will solve two main problems - scalability and adaptability. The methods and techniques used today do not sufficiently provide solutions to these complex problems. Thus, the scientific task of improving the effectiveness of methods and tools for managing problem-oriented distributed computing in a cluster Grid system, integrated with traditional meta-planners and local resource managers of Grid nodes, corresponding to trends in the concept of scalability and adaptability.

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Stepien, Slawomir Jan, Paulina Superczynska, Damian Dobrowolski, and Jerzy Dobrowolski. "SDRE-based high performance feedback control for nonlinear mechatronic systems." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 4 (July 1, 2019): 1164–76. http://dx.doi.org/10.1108/compel-10-2018-0393.

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Purpose The purpose of the paper is to present modeling and control of a nonlinear mechatronic system. To solve the control problem, the modified state-dependent Riccati equation (SDRE) method is applied. The control problem is designed and analyzed using the nonlinear feedback gain strategy for the infinite time horizon problem. Design/methodology/approach As a new contribution, this paper deals with state-dependent parametrization as an effective modeling of the mechatronic system and shows how to modify the classical form of the SDRE method to reduce computational effort during feedback gain computation. The numerical example compares described methods and confirms usefulness of the proposed technique. Findings The proposed control technique can ensure optimal dynamic response, reducing computational effort during control law computation. The effectiveness of the proposed control strategy is verified via numerical simulation. Originality/value The authors introduced an innovative approach to the well-known SDRE control methodology and settled their research in the newest literature coverage for this issue.

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Huang, Zheng-Ge. "Efficient block splitting iteration methods for solving a class of complex symmetric linear systems." Journal of Computational and Applied Mathematics 395 (October 2021): 113574. http://dx.doi.org/10.1016/j.cam.2021.113574.

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Cortés, Fernando, and María Jesús Elejabarrieta. "Computational methods for complex eigenproblems in finite element analysis of structural systems with viscoelastic damping treatments." Computer Methods in Applied Mechanics and Engineering 195, no. 44-47 (September 2006): 6448–62. http://dx.doi.org/10.1016/j.cma.2006.01.006.

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Kruse, R., and M. Steinbrecher. "Visual data analysis with computational intelligence methods." Bulletin of the Polish Academy of Sciences: Technical Sciences 58, no. 3 (September 1, 2010): 393–401. http://dx.doi.org/10.2478/v10175-010-0037-z.

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Visual data analysis with computational intelligence methodsVisual data analysis is an appealing and increasing field of application. We present two related visual analysis approaches that allow for the visualization of graphical model parameters and time-dependent association rules. When the graphical model is defined over purely nominal attributes, its local structure can be interpreted as an association rule. Such association rules comprise one of the most prominent and wide-spread analysis techniques for pattern detection, however, there are only few visualization methods. We introduce an alternative visual representation that also incorporates time since patterns are likely to change over time when the underlying data was collected from real-world processes. We apply the technique to both an artificial and a complex real-life dataset and show that the combined automatic and visual approach gives more and faster insight into the data than a fully-automatic approach only. Thus, our proposed method is capable of reducing considerably the analysis time.

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Skowron, Andrzej, Andrzej Jankowski, and Soma Dutta. "Interactive granular computing." Granular Computing 1, no. 2 (January 5, 2016): 95–113. http://dx.doi.org/10.1007/s41066-015-0002-1.

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Abstract Decision support in solving problems related to complex systems requires relevant computation models for the agents as well as methods for reasoning on properties of computations performed by agents. Agents are performing computations on complex objects [e.g., (behavioral) patterns, classifiers, clusters, structural objects, sets of rules, aggregation operations, (approximate) reasoning schemes]. In Granular Computing (GrC), all such constructed and/or induced objects are called granules. To model interactive computations performed by agents, crucial for the complex systems, we extend the existing GrC approach to Interactive Granular Computing (IGrC) approach by introducing complex granules (c-granules or granules, for short). Many advanced tasks, concerning complex systems, may be classified as control tasks performed by agents aiming at achieving the high-quality computational trajectories relative to the considered quality measures defined over the trajectories. Here, new challenges are to develop strategies to control, predict, and bound the behavior of the system. We propose to investigate these challenges using the IGrC framework. The reasoning, which aims at controlling of computations, to achieve the required targets, is called an adaptive judgement. This reasoning deals with granules and computations over them. Adaptive judgement is more than a mixture of reasoning based on deduction, induction and abduction. Due to the uncertainty the agents generally cannot predict exactly the results of actions (or plans). Moreover, the approximations of the complex vague concepts initiating actions (or plans) are drifting with time. Hence, adaptive strategies for evolving approximations of concepts are needed. In particular, the adaptive judgement is very much needed in the efficiency management of granular computations, carried out by agents, for risk assessment, risk treatment, and cost/benefit analysis. In the paper, we emphasize the role of the rough set-based methods in IGrC. The discussed approach is a step towards realization of the Wisdom Technology (WisTech) program, and is developed over years, based on the work experience on different real-life projects.

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Chen, Min-Hong, Wei Dou, and Qing-Biao Wu. "DPMHSS-based iteration methods for solving weakly nonlinear systems with complex coefficient matrices." Applied Numerical Mathematics 146 (December 2019): 328–41. http://dx.doi.org/10.1016/j.apnum.2019.07.018.

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Amelin, Konstantin, Oleg Granichin, Anna Leonova, Vikentiy Pankov, Denis Uzhva, Victoria Erofeeva, and Vladislav Ershov. "A new method of adaptive mesoscale control in complex multiagent networked dynamical systems." Cybernetics and Physics, Volume 11, 2022, Number 4 (December 30, 2022): 175–89. http://dx.doi.org/10.35470/2226-4116-2022-11-4-175-189.

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Centralized strategies applied to large-scale systems require a vast amount of computational and communication resources. In contrast to them, distributed strategies offer higher scalability and reliability. However, communication and coordination among agents tremendously impact performance of systems controlled in the distributed manner. The existing methods lead to clustering, where the coordination between agents is limited to groups of entities to be controlled. The size of these groups are usually known in advance. In turn, many systems exhibit self-organization and dynamically form clustering structure. In that sense, control methods should adapt to such dynamic structures offering the same balance between performance and communication/computational demands. In this paper, we propose a new approach to complex system control based on efficient cluster (mesoscopic) control paradigm. We demonstrate its efficacy in scenarios, where a group of agents should reach a certain goal.

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Dawson, Richard, and Jim Hall. "Adaptive importance sampling for risk analysis of complex infrastructure systems." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 462, no. 2075 (May 25, 2006): 3343–62. http://dx.doi.org/10.1098/rspa.2006.1720.

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Complex civil infrastructure systems are typically exposed to random loadings and have a large number of possible failure modes, which often exhibit spatially and temporally variable consequences. Monte Carlo (level III) reliability methods are attractive because of their flexibility and robustness, yet computational expense may be prohibitive, in which case variance reduction methods are required. In the importance sampling methodology presented here, the joint probability distribution of the loading variables is sampled according to the contribution that a given region in the joint space makes to risk, rather than according to probability of failure, which is the conventional importance sampling criterion in structural reliability analysis. Results from simulations are used to intermittently update the importance sampling density function based on the evaluations of the (initially unknown) risk function. The methodology is demonstrated on a propped cantilever beam system and then on a real coastal dike infrastructure system in the UK. The case study demonstrates that risk can be a complex function of loadings, the resistance and interactions of system components and the spatially variable damage associated with different modes of system failure. The methodology is applicable in general to Monte Carlo risk analysis of systems, but it is likely to be most beneficial where consequences of failure are a nonlinear function of load and where system simulation requires significant computational resources.

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Ahuja, Vineet, Ashvin Hosangadi, Jeremy Shipman, Russell Daines, and Jody Woods. "Multi-Element Unstructured Analyses of Complex Valve Systems." Journal of Fluids Engineering 128, no. 4 (August 24, 2005): 707–16. http://dx.doi.org/10.1115/1.2170119.

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The safe and reliable operation of high-pressure test stands for rocket engine and component testing places an increased emphasis on the performance of control valves and flow metering devices. In this paper, we will present a series of high-fidelity computational analyses of systems ranging from cryogenic control valves and pressure regulator systems to cavitating venturis that are used to support rocket engine and component testing at NASA Stennis Space Center. A generalized multi-element framework with submodels for grid adaption, grid movement, and multi-phase flow dynamics has been used to carry out the simulations. Such a framework provides the flexibility of resolving the structural and functional complexities that are typically associated with valve-based high-pressure feed systems and have been difficult to deal with using traditional computational fluid dynamics methods. Our simulations revealed a rich variety of flow phenomena such as secondary flow patterns, hydrodynamic instabilities, fluctuating vapor pockets, etc. In the paper, we will discuss performance losses related to cryogenic control valves and provide insight into the physics of the dominant multi-phase fluid transport phenomena that are responsible for the “choking-like” behavior in cryogenic control elements. Additionally, we will provide detailed analyses of the modal instability that is observed in the operation of a pressure regulator valve. Such instabilities are usually not localized and manifest themselves as a system-wide phenomena leading to an undesirable chatter at high flow conditions.

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Gu, Shuo, and Jianfeng Pei. "Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective." Evidence-Based Complementary and Alternative Medicine 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/7198645.

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With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

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Darmon, David. "Discrete Information Dynamics with Confidence via the Computational Mechanics Bootstrap: Confidence Sets and Significance Tests for Information-Dynamic Measures." Entropy 22, no. 7 (July 17, 2020): 782. http://dx.doi.org/10.3390/e22070782.

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Information dynamics and computational mechanics provide a suite of measures for assessing the information- and computation-theoretic properties of complex systems in the absence of mechanistic models. However, both approaches lack a core set of inferential tools needed to make them more broadly useful for analyzing real-world systems, namely reliable methods for constructing confidence sets and hypothesis tests for their underlying measures. We develop the computational mechanics bootstrap, a bootstrap method for constructing confidence sets and significance tests for information-dynamic measures via confidence distributions using estimates of ϵ -machines inferred via the Causal State Splitting Reconstruction (CSSR) algorithm. Via Monte Carlo simulation, we compare the inferential properties of the computational mechanics bootstrap to a Markov model bootstrap. The computational mechanics bootstrap is shown to have desirable inferential properties for a collection of model systems and generally outperforms the Markov model bootstrap. Finally, we perform an in silico experiment to assess the computational mechanics bootstrap’s performance on a corpus of ϵ -machines derived from the activity patterns of fifteen-thousand Twitter users.

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Jose, Editha C., Dylan Antonio SJ Talabis, and Eduardo R. Mendoza. "Absolutely Complex Balanced Kinetic Systems." MATCH Communications in Mathematical and in Computer Chemistry 88, no. 2 (2022): 397–436. http://dx.doi.org/10.46793/match.88-2.397j.

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A complex balanced kinetic system is absolutely complex balanced (ACB) if every positive equilibrium is complex balanced. Two results on absolute complex balancing were foundational for modern chemical reaction network theory (CRNT): in 1972, M. Feinberg proved that any deficiency zero complex balanced system is absolutely complex balanced. In the same year, F. Horn and R. Jackson showed that the (full) converse of the result is not true: any complex balanced mass action system, regardless of its deficiency, is absolutely complex balanced. In this paper, we present initial results on the extension of the Horn and Jackson ACB Theorem. In particular, we focus on other kinetic systems with positive deficiency where complex balancing implies absolute complex balancing. While doing so, we found out that complex balanced power law reactant determined kinetic systems (PL-RDK) systems are not ACB. In our search for necessary and sufficient conditions for complex balanced systems to be absolutely complex balanced, we came across the so-called CLP systems (complex balanced systems with a desired "log parametrization" property). It is shown that complex balanced systems with bi-LP property are absolutely complex balanced. For non-CLP systems, we discuss novel methods for finding sufficient conditions for ACB in kinetic systems containing non-CLP systems: decompositions, the Positive Function Factor (PFF) and the Coset Intersection Count (CIC) and their application to poly-PL and Hill-type systems.

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Botello-Smith, Wesley M., Qin Cai, and Ray Luo. "Biological applications of classical electrostatics methods." Journal of Theoretical and Computational Chemistry 13, no. 03 (May 2014): 1440008. http://dx.doi.org/10.1142/s0219633614400082.

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Continuum electrostatics modeling of solvation based on the Poisson–Boltzmann (PB) equation has gained wide acceptance in biomolecular applications such as energetic analysis and structural visualization. Successful application of the PB solvent models requires careful calibration of the solvation parameters. Extensive testing and validation is also important to ensure accuracy in their applications. Limitation in the continuum modeling of solvation is also a known issue in certain biomolecular applications. Growing interest in membrane systems has further spurred developmental efforts to allow inclusion of membrane in the PB solvent models. Despite their past successes due to careful parameterization, algorithm development and parallel implementation, there is still much to be done to improve their transferability from the small molecular systems upon which they were developed and validated to complex macromolecular systems as advances in technology continue to push forward, providing ever greater computational resources to researchers to study more interesting biological systems of higher complexity.

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Journal articles on the topic 'Computational methods for Complex Systems'

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