## What is/are System Modeling?

System Modeling - The development and implementation of the coupled electrothermal and system modeling methodology are fully detailed.^{[1]}These findings challenge the traditional use of empirical trait-Vc,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterizing Vc,max and multi-trait variability, with critical insights into ecosystem modeling and functional trait ecology.

^{[2]}The development of the software system uses the Waterfall method, with the Unified Modeling Language as the system modeling language.

^{[3]}The article provides an overview of knowledge on how to model fuel cells using software which is oriented to system modeling.

^{[4]}Concerning this issue, this article proposes a novel intelligent diagnosis method based on long short-term memory network, which deals with extracting long-term dependencies in time series effectively and learns hidden fault characteristics from traction converter multisensor signals adaptively, without needs of expert knowledge or system modeling.

^{[5]}Findings The genesis of system modeling is considered in the aspect of the evolution of language tools in the direction of objectification.

^{[6]}In the present work, we propose a measurement and a system modeling approach which relies on two acoustic measurements, namely reflection coefficients, only at the cold (burner upstream) part of the combustion appliance.

^{[7]}Smooth fuzzy systems are the new structures of the fuzzy system which have recently taken attention for their capacity in system modeling.

^{[8]}The results show promise for data-drivenfault diagnostics and system modeling.

^{[9]}Rebranding the discipline as a component of ecosystem modeling, scientists successfully brought phenology within the purview of mainstream ecology.

^{[10]}One of the research approaches is system modeling, while a virtual modeling system is used as a software tool for synthesizing a virtual physical model of a robot, and a package of application programs for solving technical computing problems MATLAB and a graphical environment for simulation Simulink are used to model a control system.

^{[11]}Understanding long-term vegetation diversity patterns and their potential responses to climate and/or human driven processes are important for ecosystem modeling and conservation.

^{[12]}The wealth of these long-term observations provides an important resource for ecosystem modeling, but there has been a lack of focus on the development of numerical models that simulate time-evolving plankton dynamics over the austral growth season along the coastal WAP.

^{[13]}An Ecopath ecosystem modeling was applied to describe the trophic model of coastal fisheries ecosystem of the northern Persian Gulf.

^{[14]}Developed a process for the preparation of the data on pollution from road transport for the system modeling “Calpuff”.

^{[15]}In building a system using the SDLC system development method with the Waterfall model, for system modeling using UML (Unified Modeling Language) and the software used in building this system using the PHP (Hypertext Preprocessor) programming language and XAMPP as a connection to the database, namely MySQL.

^{[16]}As a consequence, the attacker actions on the sensor-to-controller channel could lead to significantly erroneous data not complying with the expected evolution of the system modeling.

^{[17]}We have developed an approach that connects a complex and widely used scientific ecosystem modeling approach with a game engine for real-time communication and visualization of scientific results.

^{[18]}This confirms ecosystem modeling simulations that, while entering decadal stage of forest recovery, forest structure may differ depending on the forest type, and that the forest types that contain lodgepole pines may encounter an earlier onset of potential N limitation.

^{[19]}An example of a system modeling method in UML and its extension, which is SysML, was also presented.

^{[20]}The concept and connotation of CPS are analyzed firstly in the paper, then the challenges of system modeling brought by heterogeneous feature of the CPS system are pointed out.

^{[21]}With the system modeling and simulation, analyzed the mechanisms of signal transmission and storage, and phase-controlling, and researched the relationship between time-delay and phase-delay.

^{[22]}Numerical results showed that human errors during checkpointing both decreased system availability and brought a significant effect on the optimal rejuvenation-trigger timing, so that it should not be overlooked during system modeling.

^{[23]}Developing and optimizing fuzzy relation equations are of great relevance in system modeling, which involves analysis of numerous fuzzy rules.

^{[24]}The paper establishes a methodology to overcome the difficulty of dynamic frame alignment and system separation in impedance modeling of ac grids, and thereby enables impedance-based whole-system modeling of generator-converter composite power systems.

^{[25]}While storage is often an essential unit for system functions, none of the existing works on standby systems with reusable elements have considered this unit in the system modeling and analysis.

^{[26]}As a consequence, countermeasures on the sensor-to-controller channel could lead to significantly erroneous data not complying with the expected evolution of the system modeling.

^{[27]}To this end, system modeling was implemented to study the tradeoff between system sensitivity or range and power, gain, antenna aperture size and number of sub-array elements.

^{[28]}The objective is to design a control to drive the tank stability control system to follow a pre-specified stability constraint approximately: with the consideration of (possibly) time-varying uncertainty (including system modeling error and road excitation), if the barrel deviates from the target angle, drive it to be arbitrarily close to the target angle; and if the barrel points to the target angle, drive it to stay there.

^{[29]}Pressure controllers are designed based on system modeling and characteristics analysis.

^{[30]}His current research interest focuses on problems involving statistical signal processing and importing methods from Telecommunication Engineering and Computer Science to other disciplines in order to improve the efficiency and the information power of system modeling and analysis.

^{[31]}Modern educational courses follow these trends and generally combine the teaching of fundamental computational methods of signal and system modeling with applications to selected case studies.

^{[32]}In relation to ecosystem modeling, vegetational formations located in the center and south of the country could be expected to decrease, while vegetational formations in the north and center of the country could extend their surface area.

^{[33]}At present, frame theory has been widely used in signal and image processing, filter bank theory, coding and communications, system modeling and so on.

^{[34]}The system modeling and control design procedure along with the derivation of the existence and stability conditions are presented.

^{[35]}As a result of system modeling we built a global model of the integrated security system as an information system.

^{[36]}The writers designed a system modeling in ETAP software to design a micro-grid interconnection system that utilized the potential sources of solar and wind energy in Indonesia.

^{[37]}This information system uses the SDLC method - Prototype Model with system modeling using UML, while the implementation of the system uses the CodeIgniter framework with the MySQL database.

^{[38]}Examples of system modeling are given.

^{[39]}A numerical example of a quadrupled water tank process and practice application to system modeling of a distillation tower are employed to illustrate the proposed REM-KF algorithm's effectiveness.

^{[40]}Modeling of solar dish collector, phase change material energy storage, and thermoelectric generator sub-systems were performed in MATLAB engineering software, while solid oxide electrolysis cell sub-system modeling was carried out both in ASPEN HYSYS and MATLAB software.

^{[41]}Hence, forest disturbances cannot be neglected but should be emphasized in future forest ecosystem modeling or analyzing.

^{[42]}In this regard, system modeling and the assumptions made in different studies play a significant role in affecting the results of the study.

^{[43]}Ecopath with Ecosim (EwE) is an ecosystem modeling software that presents interactions/changes in the food web as a result of fishing.

^{[44]}Starting from System Modeling Language (SysML), the process includes mechanisms to extract hardware and software features and carry out a set of deployment candidates.

^{[45]}The System Modeling Language (SysML) used the Requirement Diagram to model non-functional requirements, such as response time, size, or system functionality, which cannot be accommodated in the Unified Modeling Language (UML).

^{[46]}This paper presents our research on semantic mapping from System Modeling Language (SysML) to Functional Reactive Programming (FRP) with the goal of developing computing mechanisms with functional reactive programming to support executable and verifiable SysML model specification.

^{[47]}Large sharks and rays are generally understudied in the Mediterranean Sea, thus leading to a knowledge gap of basic biological characteristics that are important in fisheries management and ecosystem modeling.

^{[48]}System modeling and simulation were examined with Matlab.

^{[49]}Keywords: Thermal device, MISO, energy balance equation, system modeling.

^{[50]}

## + u ⋅

This paper deals with the following quasilinear Keller-Segel-Navier-Stokes system modeling coral fertilization (*) { n t + u ⋅ ∇ n = Δ n − ∇ ⋅ ( n S ( x , n , c ) ∇ c ) − n m , x ∈ Ω , t > 0 , c t + u ⋅ ∇ c = Δ c − c + m , x ∈ Ω , t > 0 , m t + u ⋅ ∇ m = Δ m − n m , x ∈ Ω , t > 0 , u t + κ ( u ⋅ ∇ ) u + ∇ P = Δ u + ( n + m ) ∇ ϕ , x ∈ Ω , t > 0 , ∇ ⋅ u = 0 , x ∈ Ω , t > 0 under no-flux boundary conditions in a bounded domain Ω ⊂ R 3 with smooth boundary, where ϕ ∈ W 2 , ∞ ( Ω ).^{[1]}

## Earth System Modeling

The recent integration of dynamic crop growth in the Noah land surface model with multiparameterization (Noah-MP-Crop) has the potential to substantially advance Earth system modeling and is included in the latest release of the Weather Research and Forecasting (WRF) regional climate model.^{[1]}van Vuuren, and Ricarda Winkelmann Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany, Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany, Amsterdam Institute for Social Science Research, Amsterdam University, the Netherlands, IHE Delft Institute for Water Education, Delft, the Netherlands, Global Systems Institute, University of Exeter, Exeter, UK, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China, China Meteorological Administration, Beijing, China, University of Chinese Academy of Sciences, Beijing, China, Future Earth, c/o Royal Swedish Academy of Sciences, Stockholm, Sweden, Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden, International Institute for Applied Systems Analysis, Laxenburg, Austria, Fenner School of Environment & Society, Australian National University, Canberra, Australia, Center for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru, India, Center for Environmental Systems Research, Kassel University, Germany, Australian Rivers Institute, Griffith University, Brisbane, Australia, Alliance of Bioversity and CIAT, Montpellier, France, EAT, Oslo, Norway, Center for Health & the Global Environment, University of Washington, Seattle, WA, USA, Department of Earth System Science, Ministry of Education Key Laboratory of Earth System Modeling, Tsinghua University, Beijing, China, Tsinghua Urban Institute, Tsinghua University, Beijing, China, Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University, Beijing, China, Institute for Environment and Sanitation Studies, University of Ghana, Legon, Ghana, Graduate School of Media and Governance, Keio University, Fujisawa, Japan, School of Geography and Development, The University of Arizona, Tucson, AZ, USA, CORDIO East Africa, Mombasa, Kenya, Scripps Institution of Oceanography, University of California at San Diego, San Diego, CA, USA, Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland, PBL Netherlands Environmental Assessment Agency, The Hague, the Netherlands, Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, the Netherlands, Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany Key Points: • An integrated people and planet perspective is required to guide human development and use of the global commons • We outline an approach to defining a safe and just corridor for a stable and resilient planet supporting human development • A conceptual framework for linking safe and just Earth system targets is proposed.

^{[2]}Earth system modeling of climate geoengineering proposals suggests that the physical outcomes of such interventions will depend on the particulars of the implementation.

^{[3]}Performing Data Assimilation (DA) at a low cost is of prime concern in Earth system modeling, particularly at the time of big data where huge quantities of observations are available.

^{[4]}The surface mass balance scheme dEBM (diurnal Energy Balance Model) provides a novel interface between the atmosphere and land ice for Earth system modeling, which is based on the energy balance of glaciated surfaces.

^{[5]}In this review, Earth system modeling, paleoaltimetry proxies and fossil finds contribute to a new synthetic view of the topographic evolution of Tibet.

^{[6]}Freva – Free Evaluation System Framework for Earth system modeling is an efficient solution to handle evaluation systems of research projects, institutes or universities in the climate community.

^{[7]}: Earth system modeling with endogenous and dynamic human societies: the copan:CORE open World–Earth modeling framework, Earth Syst.

^{[8]}Earth system modeling is virtually impossible without dedicated data analysis.

^{[9]}A comprehensive mechanistic understanding of this history requires insights from oceanography, marine geology, geochemistry, geomicrobiology, evolutionary ecology, and Earth system modeling.

^{[10]}Two soil datasets are considered: State Soil Geographic dataset (STATSGO) from the United States Department of Agriculture and Global Soil Dataset for Earth System Modeling (GSDE) from Beijing Normal University.

^{[11]}Freva efficiently frames the interaction between different technologies thus improving the Earth system modeling science.

^{[12]}The outgoing editor in chief of JAMES reflects on his time at the journal, recent developments in Earth system modeling, and the challenges of making modeling data accessible.

^{[13]}Science challenge: Several problems in earth system modeling are dependent on highly multiscale phenomena, such as turbulence, where computational modeling is challenging and expensive.

^{[14]}The predictability of the current earth system modeling is hampered by some critical scientific gaps, including the difficulty of capturing processes and subgrid-processes across scales, mismatch of data and model resolutions, inconsistency of system and subsystem complexities, and lack of coupling with the human system.

^{[15]}Recent advances in observations and dedicated regional and earth system modeling activities over the region are also discussed alongside other emerging methodologies and tools, which can lead to the overall improvement in understanding of physical processes.

^{[16]}We show that the OAM product by the Earth System Modeling Group at GeoForschungsZentrum Potsdam has spurious short period variance in its equatorial motion terms, rendering the series a poor choice for describing oceanic signals in polar motion on time scales of less than ∼\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}2 weeks.

^{[17]}Thus far Earth system modeling efforts have primarily focused on projected mean state changes and the sensitivity of specific modes of climate variability, such as the El Niño-Southern Oscillation.

^{[18]}The ever-increasing number of regional and global datasets of terrestrial plants, such as leaf area index and chlorophyll contents, will help parameterize the land model and improve future Earth System modeling in general.

^{[19]}Find out about the person taking the helm of AGU’s dedicated earth system modeling journal, JAMES, and his vision for the coming years.

^{[20]}The Energy Exascale Earth System Model (E3SM) Project is an ongoing, state-of-the-science Earth system modeling, simulation, and prediction project that targets efficient utilization of U.

^{[21]}Science challenge: We posit that AI methods can be leveraged to significantly enhance the predictive skill of forward Earth system modeling (ESM) activities.

^{[22]}Earth system modeling of the hydrological cycle involves compute-intensive modules representing complex chemical and physical process.

^{[23]}Thus far Earth system modeling efforts have primarily focused on projected mean state changes and the sensitivity of specific modes of climate variability, such as the El Niño-Southern Oscillation.

^{[24]}A 1 km global cropland dataset from 10000 BCE to 2100 CE Bowen Cao, Le Yu, Xuecao Li, Min Chen, Xia Li, Peng Gong Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China Ministry of Education Ecological Field Station for East Asian Migratory Birds, Beijing 100084, China 5 College of Land Science and Technology, China Agricultural University, Beijing 100083, China Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 537061598, USA Nelson Institute Center for Climatic Research, University of Wisconsin-Madison, 1225 W.

^{[25]}However, the sensitivity of land-surface processes to BBCP in Earth system modeling and integrated hydrologic modeling under conditions of human disturbance has not been investigated.

^{[26]}Contributions are invited to a new journal special collection on the use of new machine learning methodologies and applications of machine learning to Earth system modeling.

^{[27]}The main part is dedicated to potentials and applications of the derived geoinformation in the various environmental research domains, such as long-term land change monitoring, sustainability research, and Earth system modeling for studying the complex human-environment interactions between land, climate change, ecosystem and biodiversity changes during the Anthropocene.

^{[28]}Soil moisture predictability on seasonal to decadal (S2D) continuum timescales over North America is examined from the Community Earth System Modeling (CESM) experiments.

^{[29]}Ecology and Earth system modeling must be integrated for scientists to fully comprehend the role of ecological systems in driving and responding to global change.

^{[30]}HEMCO complies with the Earth System Modeling Framework (ESMF) for portability across models.

^{[31]}Monthly and daily history file output in netcdf format for 25 simulated years from two different Community Earth System Modeling simulations.

^{[32]}In this paper, we show (using boron isotopes and Earth system modeling) that the impact caused rapid ocean acidification, and that the resulting ecological collapse in the oceans had long-lasting effects for global carbon cycling and climate.

^{[33]}Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, 15 Tsinghua University, Beijing, China 7.

^{[34]}NEST can be applied at different spatial and temporal resolutions, and is designed specifically to tap into the growing body of open-access geospatial data available through national inventories and the Earth system modeling community.

^{[35]}

## Energy System Modeling

First qualitative estimations of indirect environmental effects indicate the need for subsequent research in the context of smart grids, including behavioral research and energy system modeling approaches.^{[1]}The surge of machine learning and increasing data accessibility in buildings provide great opportunities for applying machine learning to building energy system modeling and analysis.

^{[2]}We combine a multitude of data sources such as weather time series, standard load profiles, census data, movement data, and employment figures to increase the scope, validity, and reproducibility for energy system modeling.

^{[3]}Finally, areas that need further research and development in renewable and sustainable energy system modeling are also highlighted.

^{[4]}A large number of energy system modeling and planning tools are available to urban energy planners, but the majority of review studies focus on summarizing the capabilities of these tools.

^{[5]}However, this analysis also highlights many complexities in energy system modeling.

^{[6]}Proven to be simpler and more reflective than existing methods, it deals with energy system modeling, instead of the thermodynamic foundations, as the primary objective.

^{[7]}Improving energy system modeling capabilities can directly affect the quality of applied studies.

^{[8]}Since political decisions on the energy system transformation are often derived from findings of energy system modeling, it seems necessary to increasingly integrate the effects of socio-ecological aspects, such as acceptance issues in energy models.

^{[9]}Energy system modeling is, however, a complicated, task subject to large uncertainty [2].

^{[10]}Furthermore, the main barriers of energy system modeling and design are technical, socioeconomic, and political aspects, while its abundant renewable energy sources, declining cost of renewable energy and storage, and the archipelago’s characteristics are key opportunities for further development.

^{[11]}Based on load prediction technology, combined with scene generation, multi-interconnected energy system modeling and other technologies, around the integrated energy system planning and design, consider the comprehensive evaluation of the whole life cycle, an optimal configuration of the integrated energy system is formed.

^{[12]}We propose a multilevel energy system modeling, including electricity market, network congestion management, and system stability, to identify challenges for the years 2023 and 2035.

^{[13]}To take into account the interconnections between the networks, this paper focuses on integrated energy system modeling and state estimation (SE) algorithm, and studies the relevant SE theories.

^{[14]}It first integrates energy system modeling with a multidimensional impact assessment that focuses on life cycle-based environmental and macroeconomic impacts.

^{[15]}Energy system modeling is essential in analyzing present and future system configurations motivated by the energy transition.

^{[16]}This research develops a novel framework that integrates market penetration modelling, long-term bottom-up energy system modeling, and life cycle assessment to identify marginal suppliers as a consequence of policy decisions in energy systems.

^{[17]}To provide reliable mitigation options for space heating, domestic hot water, industrial process heat and biomass for cooking for the energy transition time frame up to the year 2050, energy system modeling relies on a comprehensive and detailed heat demand database in high spatial resolution, which is not available.

^{[18]}

## Power System Modeling

Advances in wind power system modeling have produced widespread socioeconomic benefits for alleviating global environmental problems.^{[1]}Accurate model of generating unit plays a key role in power system modeling and simulation.

^{[2]}Accurate model of generating unit plays a key role in power system modeling and simulation.

^{[3]}Through the use of power system modeling, multi-time scale simulation, digitalanalog hybrid real-time simulation and other three aspects to summarize the status of simulation technology, and through the development of AC and DC power grid needs to analyze.

^{[4]}Finally, this paper proposes a framework for long-term electrical power system modeling considering ES and low-carbon power generation, which we have named the long-term power flow electrical power system framework.

^{[5]}In this paper, the power system modeling simulation of the grounding system via arc extinguishing coil and the transient characteristics when single-phase grounding occurs are studied.

^{[6]}This study aims to establish and propose procedures, a test protocol, and a methodology of HILS-DPT and large-scale power system modeling for testing.

^{[7]}This chapter addresses the problem of power system modeling, with emphasis on the development of aggregate microgrid (MG) models for electromechanical stability studies.

^{[8]}To analyze the grid integration effect of a virtual synchronous generator (VSG), this paper proposes a simple power system modeling method.

^{[9]}A number of system components are taken into consideration when creating protective settings: capacitive/reactive series elements, accurate power system modeling, varying standards between interfacing utilities, etc.

^{[10]}Power system modeling is software management tool for managing electricity demand, power system tradingelectricity and power system generation expansion planning purposes, through the combination of various models andtheir comparison, which can be used by the Government for its policy support and by the power enterprises for their businessdevelopment planning decision support, and ensure that power enterprises can provide sufficient and safe qualitypower supplies at the lowest economic and environmental costs.

^{[11]}The result will provide some support for subsequent electric vehicle power system modeling and SOC/SOE/SOH estimation.

^{[12]}Due to the maturity of wide area measurement technology, PMU based Dynamic Equivalent Modeling Method (PDM) has been wildly used in large scale power system modeling.

^{[13]}The results highlight both the good behavior of the MPC control technique, even in conditions that traditional emergency control would not support, and the robustness of the proposed structure against power system modeling approximations.

^{[14]}

## Nonlinear System Modeling

Adopting a student-centered teaching approach, and through the self-interactive virtual simulation experiment, the students master the classical nonlinear system modeling, analysis and controller design methods, and the teaching effect of “With the virtual to fill the real, deep interaction, integration of theory and practice ” is achieved.^{[1]}The varying-coefficient state-dependent autoregressive with exogenous inputs models are very useful in nonlinear system modeling and control.

^{[2]}The simulation results of nonlinear system modeling, sunspot prediction and algal bloom prediction demonstrate that the SDBMESN has good prediction performance and robustness.

^{[3]}We propose, in this paper, a framework for time series and nonlinear system modeling, called the basis function matrix-based flexible coefficient autoregressive (BFM-FCAR) model.

^{[4]}Koopman operator theory has served as the basis to extract dynamics for nonlinear system modeling and control across settings, including non-holonomic mobile robot control.

^{[5]}Deep recurrent neural networks (RNN), such as LSTM, have many advantages over forward networks for nonlinear system modeling.

^{[6]}Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling.

^{[7]}

## Dynamical System Modeling

We suggest a linear dynamical system modeling the evolution of different pathways of colorectal carcinogenesis based on the involved driver mutations.^{[1]}In this paper, we study a discrete dynamical system modeling an iterative process of choice in a group of agents between two possible outcomes.

^{[2]}Their interaction is found mathematically, a two-dimensional continuous-time dynamical system modeling a simple predator–prey food chain.

^{[3]}In the first stage, the SDT for the bearing is designed by the dynamical system modeling, updated using the data-driven autoregression approach, and estimate the performance using smart Kalman filter (SKF).

^{[4]}How to make full use of the evolution information of chaotic systems for time-series prediction is a difficult issue in dynamical system modeling.

^{[5]}In this paper, we study a discrete dynamical system modeling an iterative process of choice in a group of agents between two possible outcomes.

^{[6]}

## Petroleum System Modeling

The application of the developed methodology of determination geothermal characteristics and its integration into a model is necessary in work involving basin and petroleum system modeling to avoid serious errors, which are practically inevitable in the opposite cases.^{[1]}The obtained compositional kinetic spectra are used in petroleum system modeling of hydrocarbons generation during geological history.

^{[2]}Reliable geothermal data are required for basin and petroleum system modeling.

^{[3]}This long-lasting and multi-episodic tectono-sedimentary evolution of the Kashan-Ardestan Basin has led to the formation of a complex structural style, which must be resolved before petroleum system modeling and drilling of prospects can take place.

^{[4]}In this study, we obtained the first data on the compositional kinetics of the Bazhenov kerogen decomposition, which are essential for estimation of the amount and composition of hydrocarbon products in petroleum system modeling and for simulation of organic matter transformation in the processes of thermal enhanced oil recovery.

^{[5]}Three-dimensional basin and petroleum system modeling was conducted to investigate hydrocarbon generation, migration, and accumulation in the Iranian sector of the NW Persian Gulf.

^{[6]}

## Dynamic System Modeling

But for the cases of missing data and big noises, CNN does not work well for dynamic system modeling.^{[1]}Successful results that capture the dynamic behavior of the phase change embedded thermal system is achieved with a nonlinear dynamic system modeling approach.

^{[2]}The difficulties in dynamic system modeling and the recently developed machine learning tools have moved the attention of the structural damage assessment community towards a new direction.

^{[3]}Therefore, studies of dynamic system modeling through data-driven approaches have attracted more and more researchers’ attention.

^{[4]}In the study was suggested a new research methodology that is studied reliability using dynamic system modeling on Vensim software instead of traditional method to study the reliability of radio communication systems calculating large-scale differential equations.

^{[5]}

## Information System Modeling

Construction technology safety management under the background of BIM and information system modeling is studied in this paper.^{[1]}Water management with free satellite data and geographical information system modeling capabilities can be a valuable approach for optimizing the benefits from the available water resources to meet the requirements for agricultural lands.

^{[2]}The author presents a conceptual model of a smart city from both a general scientific viewpoint as well as in the context of information system modeling.

^{[3]}The two methods will be combined and compiled to be applied to the Academic Information System modeling or blended methods.

^{[4]}

## Accurate System Modeling

The existence of high nonlinearities, uncertainties and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design.^{[1]}The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated.

^{[2]}Furthermore, the system parameter and accurate system modeling can be acquired.

^{[3]}Accurate system modeling and identification gain importance as tasks executed by autonomously acting unmanned aerial vehicles (UAVs) get more complex and demanding.

^{[4]}

## Ode System Modeling

This paper is concerned with the existence, uniqueness and stability of nonconstant steady states of a reaction-diffusion-ODE system modeling macroalgae-herbivore interaction with strong Allee effect in macroalgae.^{[1]}The purpose here is to examine this efficacy by representing and analyzing a non-linear ODE system modeling potential smokers, tobacco smokers, e-cigarette smokers and quitters.

^{[2]}Also, the coupled parabolic PDE and ODE system modeling the cardiac electric activity in a monodomain system representation of cardiac tissue with different ionic models (at cell level), viz.

^{[3]}

## Complex System Modeling

To end this, it is convenient for scholars to build complex system modeling with switched topology.^{[1]}The experimental results show that complex system modeling based on big data has a positive impact on the human capital accumulation of high-tech enterprises.

^{[2]}This paper innovatively analyses the structure system of main workshop of refuse incineration power plant, which is composed of steel structure and concrete structure, and describes the skills and key points of complex system modeling.

^{[3]}

## Diffusion System Modeling

This work studies the global existence of weak solutions to a doubly haptotactic cross-diffusion system modeling oncolytic viral therapy.^{[1]}The proposed mathematical model is governed by a reaction–diffusion system modeling the interaction between the cell density and the concentration of the chemoattractant.

^{[2]}This work deals with a general cross-diffusion system modeling the dynamics behavior of two predators and one prey with signal-dependent diffusion and sensitivity subject to homogeneous Neumann boundary conditions.

^{[3]}

## Optical System Modeling

In this paper a method for transient dynamical-thermal-optical system modeling and simulation is introduced.^{[1]}It is of great concern to take such effects into optical system modeling and design.

^{[2]}

## Without System Modeling

We propose a novel, to the best of our knowledge, eigenvalue calibration method for DRC-MMP without system modeling.^{[1]}In this paper, a deep reinforcement learning based (DRL) method is presented to provide a near optimal solution to the OAPD problem without system modeling.

^{[2]}

## Existing System Modeling

We present existing system modeling approaches, simulation tools and computing frameworks, and stress the importance of FTRT accuracy.^{[1]}Existing system modeling results are expected to be able to support the service of processing academic data automatically and produce accurate and accurate statistical data and be able to reduce data manipulation factors from irresponsible parties.

^{[2]}

## Electricity System Modeling

1 This study investigates how the inclusion of frequency control constraints in electricity system modeling impacts the levels of investment and dispatch in electricity generation and storage technologies for futures that include high-level penetration of variable renewable energy.^{[1]}electricity system modeling.

^{[2]}

## Distribution System Modeling

This paper presents three-phase unbalanced radial distribution system modeling and simulation.^{[1]}Socioeconomic characteristics are influencing the temporal and spatial variability of water demand, which are the biggest source of uncertainties within water distribution system modeling.

^{[2]}

## Stoke System Modeling

This paper deals with the following quasilinear Keller-Segel-Navier-Stokes system modeling coral fertilization (*) { n t + u ⋅ ∇ n = Δ n − ∇ ⋅ ( n S ( x , n , c ) ∇ c ) − n m , x ∈ Ω , t > 0 , c t + u ⋅ ∇ c = Δ c − c + m , x ∈ Ω , t > 0 , m t + u ⋅ ∇ m = Δ m − n m , x ∈ Ω , t > 0 , u t + κ ( u ⋅ ∇ ) u + ∇ P = Δ u + ( n + m ) ∇ ϕ , x ∈ Ω , t > 0 , ∇ ⋅ u = 0 , x ∈ Ω , t > 0 under no-flux boundary conditions in a bounded domain Ω ⊂ R 3 with smooth boundary, where ϕ ∈ W 2 , ∞ ( Ω ).^{[1]}We study the chemotaxis-(Navier–)Stokes system modeling coral fertilization: $$n_t+u\cdot \nabla n=\Delta n-\nabla \cdot (nS(x,n,c)\nabla c)-nm$$ , $$c_t+u\cdot \nabla c=\Delta c-c+m$$ , $$m_t+u\cdot \nabla m=\Delta m-nm$$ , $$u_t+\kappa (u\cdot \nabla )u+\nabla P=\Delta u+(n+m)\nabla \phi $$ and $$\nabla \cdot u=0$$ in a bounded and smooth domain $$\Omega \subset \mathbb {R}^2$$ , where $$\kappa \in \mathbb {R}$$ , $$\phi \in W^{2,\infty }(\Omega )$$ , and $$S\in C^2({\bar{\Omega }}\times [0,\infty )^2;\mathbb {R}^{2\times 2})$$ satisfies $$|S(x,n,c)|\le S_0(c)(1+n)^{-\alpha }$$ for all $$(x,n,c)\in {\bar{\Omega }}\times [0,\infty )^2$$ with $$\alpha \in \mathbb {R}$$ and the function $$S_0:[0,\infty )\rightarrow [0,\infty )$$ nondecreasing.

^{[2]}