Relevant bibliographies by topics / Dynamic Graph Metrics

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Dynamic Graph Metrics'

Author: Grafiati
Published: 25 January 2025

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Journal articles on the topic "Dynamic Graph Metrics"

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Sizemore, Ann E., and Danielle S. Bassett. "Dynamic graph metrics: Tutorial, toolbox, and tale." NeuroImage 180 (October 2018): 417–27. http://dx.doi.org/10.1016/j.neuroimage.2017.06.081.

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Davis, Jacob D., and Eberhard O. Voit. "Metrics for regulated biochemical pathway systems." Bioinformatics 35, no. 12 (2018): 2118–24. http://dx.doi.org/10.1093/bioinformatics/bty942.

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Abstract Motivation The assessment of graphs through crisp numerical metrics has long been a hallmark of biological network analysis. However, typical graph metrics ignore regulatory signals that are crucially important for optimal pathway operation, for instance, in biochemical or metabolic studies. Here we introduce adjusted metrics that are applicable to both static networks and dynamic systems. Results The metrics permit quantitative characterizations of the importance of regulation in biochemical pathway systems, including systems designed for applications in synthetic biology or metaboli
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Sunarmodo, Wismu, and Bayu Distiawan Trisedya. "Anchored Self-Supervised Dynamic Graph Representation Learning for Aviation Data as A Fast Economic Indicator." International Journal on Advanced Science, Engineering and Information Technology 14, no. 6 (2024): 1842–48. https://doi.org/10.18517/ijaseit.14.6.20170.

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The Fast Economic Growth Indicator, a newly developed metric leveraging big data, provides policymakers with timely insights crucial for assessing the economic impact of policies or events. Among various open-source datasets, aviation data stands out as a potential indicator of rapid economic growth, given its inherent graph structure with airports as nodes and flight connections as edges. However, global flight data, being dynamic and complex, poses challenges in analysis. To glean comprehensive insights, it's imperative to condense this graph data into representative vector values while pres
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Antal, Gábor, Zoltán Tóth, Péter Hegedűs, and Rudolf Ferenc. "Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics." Technologies 9, no. 1 (2020): 3. http://dx.doi.org/10.3390/technologies9010003.

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Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HN
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Wang, Mingjie, Yifan Huo, Junhong Zheng, and Lili He. "SC-TKGR: Temporal Knowledge Graph-Based GNN for Recommendations in Supply Chains." Electronics 14, no. 2 (2025): 222. https://doi.org/10.3390/electronics14020222.

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Graph neural networks (GNNs) are widely used in recommendation systems to improve prediction performance, especially in scenarios with diverse behaviors and complex user interactions within supply chains. However, while existing models have achieved certain success in capturing the temporal and dynamic aspects of supply chain behaviors, challenges remain in effectively addressing the time-sensitive fluctuations of market demands and user preferences. Motivated by these challenges, we propose SC-TKGR, a supply chain recommendation framework based on temporal knowledge graphs. It employs enhance
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Ruf, Verena, Anna Horrer, Markus Berndt, et al. "A Literature Review Comparing Experts’ and Non-Experts’ Visual Processing of Graphs during Problem-Solving and Learning." Education Sciences 13, no. 2 (2023): 216. http://dx.doi.org/10.3390/educsci13020216.

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The interpretation of graphs plays a pivotal role in education because it is relevant for understanding and representing data and comprehending concepts in various domains. Accordingly, many studies examine students’ gaze behavior by comparing different levels of expertise when interpreting graphs. This literature review presents an overview of 32 articles comparing the gaze behavior of experts and non-experts during problem-solving and learning with graphs up to January 2022. Most studies analyzed students’ dwell time, fixation duration, and fixation count on macro- and meso-, as well as on m
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Liu, Liu, Sibren Isaacman, and Ulrich Kremer. "An Adaptive Application Framework with Customizable Quality Metrics." ACM Transactions on Design Automation of Electronic Systems 27, no. 2 (2022): 1–33. http://dx.doi.org/10.1145/3477428.

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Many embedded environments require applications to produce outcomes under different, potentially changing, resource constraints. Relaxing application semantics through approximations enables trading off resource usage for outcome quality. Although quality is a highly subjective notion, previous work assumes given, fixed low-level quality metrics that often lack a strong correlation to a user’s higher-level quality experience. Users may also change their minds with respect to their quality expectations depending on the resource budgets they are willing to dedicate to an execution. This motivate
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Yu, Mingqin, Fethi A. Rabhi, and Madhushi Bandara. "Ontology-Driven Architecture for Managing Environmental, Social, and Governance Metrics." Electronics 13, no. 9 (2024): 1719. http://dx.doi.org/10.3390/electronics13091719.

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The burgeoning significance of environmental, social, and governance (ESG) metrics in realms such as investment decision making, corporate reporting, and risk management underscores the imperative for a robust, comprehensive solution capable of effectively capturing, representing, and analysing the multifaceted and intricate ESG data landscape. Facing the challenge of aligning with diverse standards and utilising complex datasets, organisations require robust systems for the integration of ESG metrics with traditional financial reporting. Amidst this, the evolving regulatory landscape and the
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Chen, Yuhang, Jiaxin Jiang, Shixuan Sun, Bingsheng He, and Min Chen. "RUSH: Real-Time Burst Subgraph Detection in Dynamic Graphs." Proceedings of the VLDB Endowment 17, no. 11 (2024): 3657–65. http://dx.doi.org/10.14778/3681954.3682028.

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Graph analytics have been effective in the data science pipeline of fraud detections. In the ever-evolving landscape of e-commerce platforms like Grab or transaction networks such as cryptos, we have witnessed the phenomenon of 'burst subgraphs,' characterized by rapid increases in subgraph density within short timeframes---as a common pattern for fraud detections on dynamic graphs. However, existing graph processing frameworks struggle to efficiently manage these due to their inability to handle sudden surges in data. In this paper, we propose RUSH ( R eal-time b U rst S ubgrap H detection fr
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Mu, Bo, Guohang Tian, Gengyu Xin, et al. "Measuring Dynamic Changes in the Spatial Pattern and Connectivity of Surface Waters Based on Landscape and Graph Metrics: A Case Study of Henan Province in Central China." Land 10, no. 5 (2021): 471. http://dx.doi.org/10.3390/land10050471.

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An understanding of the scientific layout of surface water space is crucial for the sustainable development of human society and the ecological environment. The objective of this study was to use land-use/land-cover data to identify the spatiotemporal dynamic change processes and the influencing factors over the past three decades in Henan Province, central China. Multidisciplinary theories (landscape ecology and graph theory) and methods (GIS spatial analysis and SPSS correlation analysis) were used to quantify the dynamic changes in surface water pattern and connectivity. Our results reveale
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Dissertations / Theses on the topic "Dynamic Graph Metrics"

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Bridonneau, Vincent. "Generation and Analysis of Dynamic Graphs." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMLH23.

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La nature et les sociétés humaines offrent de nombreux exemples de systèmes composés d'entités qui interagissent, communiquent ou sont simplement connectées les unes aux autres. La théorie des graphes offre un excellent formalisme pour modéliser ces systèmes complexes, allant des réseaux sociaux aux systèmes biologiques. La plupart des phénomènes observés dans ces réseaux peuvent s'exprimer sous forme de propriétés sur les graphes. On peut notamment citer le phénomène du « petit monde » ou les réseaux dits « sans échelle ». Comprendre les mécanismes sous-jacents à leur évolution est essentiel
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Saman, Nariman Goran. "A Framework for Secure Structural Adaptation." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78658.

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A (self-) adaptive system is a system that can dynamically adapt its behavior or structure during execution to "adapt" to changes to its environment or the system itself. From a security standpoint, there has been some research pertaining to (self-) adaptive systems in general but not enough care has been shown towards the adaptation itself. Security of systems can be reasoned about using threat models to discover security issues in the system. Essentially that entails abstracting away details not relevant to the security of the system in order to focus on the important aspects related to secu
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Noharet, Léo, and Anton Fu. "Using simulated dynamics and graph metrics to compare brain networks of MCI patients and healthy control subjects." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301839.

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In recent years, several different methods have been proposed to compare brain networks with the joint use of graph theory and graph metrics. Another relatively unexplored comparison method is comparing the brain’s response to various input signals by simulating the brain’s dynamics. The brain activity signals are dependent on the physical connectivity patterns of the brain, therefore brain activity signals can be studied in addition to the study of connectivity patterns of brain graphs. Thus, in this study we use both static graph metrics and the dynamics of the brain to compare the brain net
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Hiransakolwong, Nualsawat. "AUTOMATIC ANNOTATION OF DATABASE IMAGES FOR QUERY-BY-CONCEPT." Doctoral diss., University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2639.

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As digital images become ubiquitous in many applications, the need for efficient and effective retrieval techniques is more demanding than ever. Query by Example (QBE) and Query by Concept (QBC) are among the most popular query models. The former model accepts example images as queries and searches for similar ones based on low-level features such as colors and textures. The latter model allows queries to be expressed in the form of high-level semantics or concept words, such as "boat" or "car," and finds images that match the specified concepts. Recent research has focused on the connections
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Zhao, Z. W., and I.-Ming Chen. "Optimizing the Dynamic Distribution of Data-stream for High Speed Communications." 2004. http://hdl.handle.net/1721.1/7459.

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The performances of high-speed network communications frequently rest with the distribution of data-stream. In this paper, a dynamic data-stream balancing architecture based on link information is introduced and discussed firstly. Then the algorithms for simultaneously acquiring the passing nodes and links of a path between any two source-destination nodes rapidly, as well as a dynamic data-stream distribution planning are proposed. Some related topics such as data fragment disposal, fair service, etc. are further studied and discussed. Besides, the performance and efficiency of proposed algor
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Jasinski, Jakub. "Hrushovski and Ramsey Properties of Classes of Finite Inner Product Structures, Finite Euclidean Metric Spaces, and Boron Trees." Thesis, 2011. http://hdl.handle.net/1807/29762.

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We investigate two combinatorial properties of classes of finite structures, as well as related applications to topological dynamics. Using the Hrushovski property of classes of finite structures -- a finite extension property of homomorphisms -- we can show the existence of ample generics. For example, Solecki proved the existence of ample generics in the context of finite metric spaces that do indeed possess this extension property. Furthermore, Kechris, Pestov and Todorcevic have shown that the Ramsey property of Fraisse classes of finite structures implies that the automorphism group of t
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Books on the topic "Dynamic Graph Metrics"

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Harmonic Analysis. American Mathematical Society, 2018.

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Book chapters on the topic "Dynamic Graph Metrics"

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Meidiana, Amyra, Seok-Hee Hong, and Peter Eades. "New Quality Metrics for Dynamic Graph Drawing." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-68766-3_35.

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Gain, Ayan, and Mridul Sankar Barik. "Attack Graph Based Security Metrics for Dynamic Networks." In Information Systems Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49099-6_7.

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Vincent, Bridonneau, Guinand Frédéric, and Pigné Yoann. "DynamicScore: A Novel Metric for Quantifying Graph Dynamics." In Complex Networks & Their Applications XII. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53499-7_35.

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O’ Mahony, Niall, Anshul Awasthi, Joseph Walsh, and Daniel Riordan. "Latent Space Cartography for Geometrically Enriched Latent Spaces." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_38.

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AbstractThere have been many developments in recent years on the exploitation of non-Euclidean geometry for the better representation of the relation between subgroups in datasets. Great progress has been made in this field of Disentangled Representation Learning, in leveraging information geometry divergence, manifold regularisation and geodesics to allow complex dynamics to be captured in the latent space of the representations produced. However, interpreting the high-dimensional latent spaces of the modern deep learning-based models involved is non-trivial. Therefore, in this paper, we investigate how techniques in Latent Space Cartography can be used to display abstract and representational 2D visualisations of manifolds.Additionally, we present a multi-task metric learning model to capture in its output representations as many metrics as is available in a multi-faceted fine-grained change detection dataset. We also implement an interactive visualisation tool that utilises cartographic techniques that allow dimensions and annotations of graphs to be representative of the underlying factors affecting individual scenarios the user can morph and transform to focus on an individual/sub-group to see how they are performing with respect to said metrics.
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Kim, Woojin, Facundo Mémoli, and Zane Smith. "Analysis of Dynamic Graphs and Dynamic Metric Spaces via Zigzag Persistence." In Topological Data Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43408-3_14.

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Burattin, Andrea, Hugo A. López, and Lasse Starklit. "Uncovering Change: A Streaming Approach for Declarative Processes." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_12.

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AbstractProcess discovery is a family of techniques that helps to comprehend processes from their data footprints. Yet, as processes change over time so should their corresponding models, and failure to do so will lead to models that under- or over-approximate behaviour. We present a discovery algorithm that extracts declarative processes as Dynamic Condition Response (DCR) graphs from event streams. Streams are monitored to generate temporal representations of the process, later processed to create declarative models. We validated the technique by identifying drifts in a publicly available dataset of event streams. The metrics extend the Jaccard similarity measure to account for process change in a declarative setting. The technique and the data used for testing are available online.
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Wang, Di. "Explainable Deep Reinforcement Learning for Knowledge Graph Reasoning." In Recent Developments in Machine and Human Intelligence. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9189-8.ch012.

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Artificial intelligence faces a considerable challenge in automated reasoning, particularly in inferring missing data from existing observations. Knowledge graph (KG) reasoning can significantly enhance the performance of context-aware AI systems such as GPT. Deep reinforcement learning (DRL), an influential framework for sequential decision-making, exhibits strength in managing uncertain and dynamic environments. Definitions of state space, action space, and reward function in DRL directly dictate the performances. This chapter provides an overview of the pipeline and advantages of leveraging DRL for knowledge graph reasoning. It delves deep into the challenges of KG reasoning and features of existing studies. This chapter offers a comparative study of widely used state spaces, action spaces, reward functions, and neural networks. Furthermore, it evaluates the pros and cons of DRL-based methodologies and compares the performances of nine benchmark models across six unique datasets and four evaluation metrics.
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Beynier, Aurélie, Jean-Guy Mailly, Nicolas Maudet, and Anaëlle Wilczynski. "Explaining the Lack of Locally Envy-Free Allocations." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240900.

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In fair division, local envy-freeness is a desirable property which has been thoroughly studied in recent years. In this paper, we study explanations which can be given to explain that no allocation of items can satisfy this criterion, in the house allocation setting where agents receive a single item. While Minimal Unsatisfiable Subsets (MUSes) are key concepts to extract explanations, they cannot be used as such: (i) they highly depend on the initial encoding of the problem; (ii) they are flat structures which fall short of capturing the dynamics of explanations; (iii) they typically come in large number and exhibit great diversity. In this paper we provide two SAT encodings of the problem which allow us to extract MUS when instances are unsatisfiable. We build a dynamic graph structure which allows to follow step-by-step the explanation. Finally, we propose several criteria to select MUSes, some of them being based on the MUS structure, while others rely on this original graphical explanation structure. We give theoretical bounds on these metrics, showing that they can vary significantly for some instances. Experimental results on synthetic data complement these results and illustrate the impact of the encodings and the relevance of our metrics to select among the many MUSes.
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Marmier, Arnaud. "Anomalous Mechanical Behaviour Arising From Framework Flexibility." In Mechanical Behaviour of Metal – Organic Framework Materials. The Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/9781839166594-00065.

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This chapter describes in detail the framework of anisotropic elasticity to introduce exotic elastic and thermoelastic properties that are often prevalent and extreme in MOFs: negative linear compressibility (NLC), negative thermal expansion (NTE) and negative Poisson's ratio (NPR). The complete elastic tensors of 56 MOFs are collated and analysed to reveal that NPR is very common in non-cubic MOF structures, but the mechanisms are not yet clear. This is in contrast to NLC and NTE, which derive from the interplay between rigid, stiff building blocks and compliant joint flexing in ‘wine-rack’ type motions and dynamic equivalents. As the existence of flexible mechanisms seems to allow large and unusual framework deformations, several potential measures of materials and framework flexibility are considered, from ad hoc elastic anisotropy metrics to graph-theoretical and group-theoretical analyses, including physical models.
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Tauch, Sotharith, William Liu, and Russel Pears. "Measuring Cascading Failures in Smart Grid Networks." In Smart Grid as a Solution for Renewable and Efficient Energy. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0072-8.ch009.

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Smart Grid is the next generation of the electrical power systems that form from interdependent networks. Cascading failures in such interdependent critical infrastructure is very crucial which can cause wide spread disruption. This chapter is intended to evaluate four different topological metrics in which can be best described and approximated the behavior of cascading failures in interdependent networks by employing two interlinks strategies such as random interlinks addition and degree-degree correlation interlinks. The four chosen topological metrics are algebraic connectivity, effective graph resistance, average betweenness centrality, and average distance. Throughout the chapter, analytical study of each metric are discussed and also compared with numerical simulation based on sandpile dynamics load distribution.
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Conference papers on the topic "Dynamic Graph Metrics"

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Cai, Borui, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, and Jianxin Li. "Temporal Knowledge Graph Completion: A Survey." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/734.

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Knowledge graph completion (KGC) predicts missing links and is crucial for real-life knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time. Emerging methods have recently shown improved prediction results by further incorporating the temporal validity of facts; namely, temporal knowledge graph completion (TKGC). With this temporal information, TKGC methods explicitly learn the dynamic evolution of the knowledge graph that KGC method
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Marsili, Valentina, Stefano Alvisi, Filomena Maietta, et al. "Characterizing the effects of water distribution system topology modifications on its dynamic behaviour through connectivity metrics." In 2nd WDSA/CCWI Joint Conference. Editorial Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/wdsa-ccwi2022.2022.14016.

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Water distribution networks (WDNs) are complex combinations of nodes and links and their structure has an impact on their behaviour, considering both quantitative (i.e. related to pipe flows and nodal pressures) and qualitative (i.e. related to water age and quality) aspects. The complexity of WDNs has been the basis of several studies that have resorted to the graph theory to relate connectivity properties to system behaviour (e.g. its reliability and water age/quality), evaluated under the assumption of steady-state conditions. Within this framework, in recent years the tendency toward reduc
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Kotecha, Maulik C., Wanyu Xu, and Daniel A. McAdams. "Exploring a Graph Theoretic Approach for the Quantitative Comparison of Digital Twin Designs." In ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/detc2024-142928.

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Abstract A graph theoretic approach for quantitatively comparing digital twin designs is explored in this paper, with a novel methodology being offered to evaluate the structural and functional similarities between these complex systems. Graph theory is leveraged to represent digital twins as mathematical graphs in our approach, with nodes represented by function-flow pairs and the interactions between the functions depicted by edges. The application of graph-based similarity measures, such as NetSimile and DeltaCon distance, for quantitatively assessing the resemblance between digital twin de
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Zhao, Sheng, Baisravan HomChaudhuri, and Manish Kumar. "A Method for Distributed Optimization for Task Allocation." In ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2694.

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Allocation of a large number of resources to tasks in a complex environment is often a very challenging problem. This is primarily due to the fact that a large number of resources to be allocated results into an optimization problem that involves a large number of decision variables. Most of the optimization algorithms suffer from this issue of non-scalability. Further, the uncertainties and dynamic nature of environment make the optimization problem quite challenging. One of the techniques to overcome the issue of scalability that have been considered recently is to carry out the optimization
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Morozov, Andrey, Mihai A. Diaconeasa, and Mikael Steurer. "A Hybrid Methodology for Model-Based Probabilistic Resilience Evaluation of Dynamic Systems." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23789.

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Abstract Advanced classical Probabilistic Risk Assessment (PRA) effectively combines various methods for quantitative risk evaluation, such as event trees, fault trees, and Bayesian networks. PRA methods and tools provide the means for the qualitative reliability evaluation (e.g., cut sets) and the computation of quantitative reliability metrics (e.g., end states probabilities). Modern safety-critical systems from various industrial domains tend toward a high level of autonomy and demand not only reliability but also resilience, the ability to recover from degraded or failed states. The numeri
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DELO, GIULIA, CECILIA SURACE, KEITH WORDEN, and DANIEL S. BRENNAN. "ON THE INFLUENCE OF STRUCTURAL ATTRIBUTES FOR ASSESSING SIMILARITY IN POPULATION-BASED STRUCTURAL HEALTH MONITORING." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/36904.

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The viability of many machine learning methods within Structural Health Monitoring (SHM) is often limited by the lack, or the incompleteness, of the data required for implementing these algorithms. Indeed, learning a data-based SHM predictive model usually requires the dynamic response availability for undamaged and damaged states, and the assumption that both training and test data refer to the same domain. In this framework, the population-based approach to Structural Health Monitoring (PBSHM) aims at improving the performance and the robustness of diagnostic inferences, exploiting the trans
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Popov, Anton I., Igor Y. Popov, Dmitri S. Nikiforov, and Irina V. Blinova. "Time-dependent metric graph: Wave dynamics." In CENTRAL EUROPEAN SYMPOSIUM ON THERMOPHYSICS 2019 (CEST). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5114299.

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Zheng, Xiaolin, Jiajie Su, Weiming Liu, and Chaochao Chen. "DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation." In MM '22: The 30th ACM International Conference on Multimedia. ACM, 2022. http://dx.doi.org/10.1145/3503161.3548072.

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Louca, Loucas S., and Evagoras Xydas. "Model Reduction of Modal Representations." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14097.

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Dynamic analysis is extensively used to study the behavior of continuous and lumped parameter linear systems. In addition to the physical space, analyses can also be performed in the modal space where useful frequency information of the system can be extracted. More specifically, modal analysis can be used for the analysis and controller design of dynamic systems, where reduction of model complexity without degrading the accuracy is often required for the efficient use of the model. The reduction of modal models has been extensively studied and many reduction techniques are available. The majo
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McCain, B. A., and A. G. Stefanopoulou. "Order Reduction for a Control-Oriented Model of the Water Dynamics in Fuel Cells." In ASME 2006 4th International Conference on Fuel Cell Science, Engineering and Technology. ASMEDC, 2006. http://dx.doi.org/10.1115/fuelcell2006-97075.

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Predicting the water dynamics and estimating humidity and flooding conditions in a low-temperature fuel cell are critical for robust operation and long life. Previous work by McKay et al [1] shows that the fuel cell anode, cathode, and membrane water dynamics and gaseous species concentrations can be accurately modeled by discretizing the partial differential equations that describe mass transport into three segments. Avoiding sensitivities associated with over-parameterization, and allowing for the real-time computations necessary for embedded controllers, requires in-depth investigation of t
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