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[null,null,"Titolo",null,"Computational Science – ICCS 2024 : 24th International Conference, Malaga, Spain, July 2–4, 2024, Proceedings, Part V / / edited by Leonardo Franco, Clélia de Mulatier, Maciej Paszynski, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M. A. Sloot"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"3-031-63775-5"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (457 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 14836"]
[null,null,"Disciplina",null,"511.3"]
[null,null,"Soggetti",null,"Computer science"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Software engineering"]
[{"$nulls":4},"Computer science - Mathematics"]
[{"$nulls":4},"Theory of Computation"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Software Engineering"]
[{"$nulls":4},"Mathematics of Computing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part V -- Computational Optimization, Modelling and Simulation -- Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Networks -- 1 Introduction -- 2 Multi-Objective Optimization Methodology -- 2.1 MO Microwave Design Optimization -- 2.2 Multi-Resolution EM Models -- 2.3 Sampling Procedure and ANN-Based Surrogate Modeling -- 2.4 Multi-Objective Evolutionary Algorithm (MOEA) -- 2.5 Infill Point Allocation -- 2.6 Management Scheme of Multi-Fidelity Models. Dataset Updating -- 2.7 Algorithm Termination -- 2.8 Algorithm Operation -- 3 Verification Case Studies -- 4 Conclusion -- References -- Expedited Machine-Learning-Based Global Design Optimization of Antenna Systems Using Response Features and Multi-fidelity EM Analysis -- 1 Introduction -- 2 "]
[{"$nulls":4},"Global Optimization Using Response Features and Multi-fidelity EM Analysis -- 2.1 Antenna Design Task: Formulation -- 2.2 Concept of Response Features -- 2.3 Multi-resolution EM Simulations -- 2.4 Kriging and Co-kriging Metamodels -- 2.5 Pre-screening of Design Space. Primary Surrogate Construction -- 2.6 PSO-Based Infill Points Generation. Co-Kriging Model -- 2.7 Complete Optimization Procedure -- 3 Verification Case Studies -- 3.1 Test Cases and Experimental Setup -- 3.2 Results and Discussion -- 4 Conclusion -- References -- Exploring Apple Silicon's Potential from Simulation and Optimization Perspective -- 1 Introduction -- 2 Apple Silicon Overview -- 3 Methodology -- 4 Experimental Results -- 5 Discussion -- 6 Conclusion -- References -- Deep Neural Network for Constraint Acquisition Through Tailored Loss Function -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Symbolic Regression -- 3.2 Loss Function Definition for Constraint Acquisition -- 3.3 EQL and Loss Function Integration."]
[{"$nulls":4},"3.4 Experiments Definition and Setup -- 4 Results and Discussion -- 5 Conclusions -- References -- Efficient Search Algorithms for the Restricted Longest Common Subsequence Problem -- 1 Introduction -- 1.1 Preliminaries -- 2 The DP Approach for the RLCS Problem -- 3 Greedy Algorithm -- 4 The Proposed Efficient Search Methods -- 4.1 The General Search Framework -- 4.2 A* Search Algorithm -- 4.3 Beam Search Algorithm -- 5 Experimental Evaluation -- 5.1 Statistical Analysis -- 6 Conclusions and Future Work -- References -- Adaptive Hyperparameter Tuning Within Neural Network-Based Efficient Global Optimization -- 1 Introduction -- 2 Methods -- 2.1 Surrogate-Based Optimization -- 2.2 Efficient Global Optimization -- 2.3 EGO Using Neural Networks -- 2.4 Hyperparameter Optimization -- 3 Numerical Experiments -- 3.1 Problem Formulation of the Hartmann Function -- 3.2 Strategies and Algorithm Setup -- 3.3 Optimizing the Full Set of HPs -- 3.4 Optimizing a Subset of the HPs -- 4 Conclusion -- References -- Hypergraph Clustering with Path-Length Awareness -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations and Definitions -- 2.2 Related Works -- 3 Model and Weighting Schemes -- 4 A Parameterized M-Approximation Algorithm for Red-Black Hypergraph Clustering -- 4.1 Binary Search Clustering (BSC) -- 4.2 Heavy-Edge Matching -- 5 Experimental Results -- 6 Conclusion -- References -- Adaptive Sampling for Non-intrusive Reduced Order Models Using Multi-task Variance -- 1 Introduction -- 2 Field-Based Adaptive Sampling -- 2.1 Proposed Adaptive Sampling Algorithm -- 2.2 Dimensionality Reduction -- 2.3 Multi-task Gaussian Process Models -- 2.4 Uncertainty Propagation -- 2.5 Scalarized Sampling Objective -- 3 Numerical Experiments -- 3.1 2D Environment Model Function -- 3.2 Comparison Methods and Metric -- 3.3 Setup of the Sampling Algorithm -- 3.4 Results."]
[{"$nulls":4},"4 Conclusion -- References -- GraphMesh: Geometrically Generalized Mesh Refinement Using GNNs -- 1 Introduction -- 2 Background -- 2.1 Mesh Generation -- 2.2 FE Solution and Simulation Error -- 2.3 Characteristic Length -- 3 Methodology -- 3.1 Overview -- 3.2 Data Preparation -- 3.3 Architectural Details -- 3.4 Training and Evaluation -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 Experiment Details -- 4.3 Governing Equation and Boundary Condition -- 4.4 Evaluation Details -- 5 Results -- 5.1 Mesh Comparison -- 5.2 Simulation Error Comparison -- 5.3 Comparison for Various Simulation Times -- 6 Discussion -- 7 Conclusion -- References -- Gradient Method for Solving Singular Optimal Control Problems -- 1 Introduction -- 2 Optimal Control Problem -- 3 Gradient Methods for Finding Optimal Control -- 4 Numerical Computations of Optimal Controls -- 4.1 "]
[{"$nulls":4},"Mathematical Model -- 4.2 Nonlinear Objective Functional -- 4.3 Optimal Control Problem -- 4.4 Numerical Optimisation -- 4.5 Comparative Analysis -- 5 Conclusions -- References -- Multiobjective Optimization of Complete Coverage and Path Planning for Emergency Response by UAVs in Disaster Areas -- 1 Introduction -- 2 Representation of a Disaster Area Map -- 3 The Problem Solution-UAVs' Paths -- 4 Evaluation of UAVs' Paths -- 5 The Optimization Method -- 6 Experimental Research -- 6.1 Plan of Experiments -- 6.2 Results of Experiments -- 7 Conclusions -- References -- Single-Scattering and Multi-scattering in Real-Time Volumetric Rendering of Clouds -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Dataset -- 3.2 Single-Scattering -- 3.3 Single-Scattering Cached -- 3.4 Multi-scattering -- 4 Experiments and Results -- 4.1 Single Scattering Brute Force -- 4.2 Single Scattering Cached -- 4.3 Multi Scattering Cached -- 4.4 Comparison with the Reference Method -- 4.5 Summary -- 5 Discussion -- 6 Conclusions."]
[{"$nulls":4},"References -- Modeling the Dynamics of a Multi-planetary System with Anisotropic Mass Variation -- 1 Introduction -- 2 Equations of Motion -- 2.1 Unperturbed Motion -- 2.2 Perturbed Motion -- 3 Evolutionary Equations -- 4 Simulation -- 5 Conclusion -- References -- Best of Both Worlds: Solving the Cyclic Bandwidth Problem by Combining Pre-existing Knowledge and Constraint Programming Techniques -- 1 Introduction -- 2 The Cyclic Bandwidth Problem and Existing Results -- 2.1 The Problem -- 2.2 Metaheuristics Results -- 2.3 Theoretical Structural Results -- 3 Recycling Algorithm: Orchestration of Existing Results -- 4 Cyclic Bandwidth as an Optimization Constrained Problem -- 4.1 Arithmetic (or Direct) Model -- 4.2 Finite Domain Extensional Constraint Model for Satisfiability -- 4.3 Possible Improvements -- 4.4 From Satisfiability to Optimization Models -- 5 Results -- 6 Conclusions and Future Work -- References -- A Novel Bandwidth Occupancy Forecasting Method for Optical Networks -- 1 Introduction -- 2 Problem Formulation -- 3 Optimisation Algorithms -- 3.1 + Algorithm -- 3.2 Bee Colony Algorithm -- 4 Data Collection and Statistical Analysis -- 5 Results and Discussion -- 6 Conclusions -- References -- Automatic Gradient Estimation for Calibrating Crowd Models with Discrete Decision Making -- 1 Introduction -- 2 Background and Related Work -- 3 DiscoGrad Gradient Oracle -- 4 Crowd Model and Scenarios -- 5 Experiments -- 5.1 Automatically Differentiating the Social Force Model -- 5.2 Calibrating Force Coefficients -- 5.3 Calibrating Decision-Making Parameters -- 6 Conclusions -- References -- Parameter Tuning of the Firefly Algorithm by Standard Monte Carlo and Quasi-Monte Carlo Methods -- 1 Introduction -- 2 Literature Review of Parameter Tuning -- 3 Tuning Parameters by MC and QMC -- 3.1 Firefly Algorithm -- 3.2 Monte Carlo Method."]
[{"$nulls":4},"3.3 Quasi-Monte Carlo Method -- 4 Experiment Setup and Benchmarks -- 4.1 Experimental Setup for FA Parameters -- 4.2 Benchmark Functions -- 5 Results and Hypothesis Testing -- 5.1 Testing the First Hypothesis -- 5.2 Testing the Second Hypothesis -- 5.3 F-Test for Variances -- 6 Conclusion and Future Work -- References -- Generative AI and Large Language Models (LLMs) in Advancing Computational Medicine -- Quantifying Similarity: Text-Mining Approaches to Evaluate ChatGPT and Google Bard Content in Relation to BioMedical Literature -- 1 Introduction and Related Work -- 2 Data -- 2.1 Large Language Models -- 2.2 Prompt Engineering -- 3 Methods -- 3.1 Text Mining Similarity Analysis -- 3.2 Networks Analysis -- 4 Results -- 4.1 Document Similarity Analysis -- 4.2 Bigram Similarity - TF-IDF Bigram Frequency Analysis -- 4.3 Bigram Networks Analysis -- "]
[{"$nulls":4},"5 Summary and Discussion -- 6 Conclusions and Future Directions -- References -- ClinLinker: Medical Entity Linking of Clinical Concept Mentions in Spanish -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Description of the Corpora -- 3.2 ClinLinker: Bi-encoder+Cross-encoder Pipeline for MEL -- 4 Results and Discussion -- 5 Conclusions -- References -- Stylometric Analysis of Large Language Model-Generated Commentaries in the Context of Medical Neuroscience -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Stylometry -- 3.2 Qualitative Criteria for Text Evaluation -- 3.3 Large Language Models Used -- 4 Data -- 4.1 The Source Papers and Commentaries -- 4.2 The Generation of LLM's Commentary (Prompts) -- 5 Results -- 5.1 Quantitative Analysis -- 5.2 Qualitative Assessment -- 6 Discussion -- 7 Conclusion -- References -- Machine Learning and Data Assimilation for Dynamical Systems -- Explainable Hybrid Semi-parametric Model for Prediction of Power Generated by Wind Turbines -- 1 Introduction."]
[{"$nulls":4},"2 Computational Methods."]
[null,null,null,"Sommario/riassunto","The 7-volume set LNCS 14832 – 14838 constitutes the proceedings of the 24th International Conference on Computational Science, ICCS 2024, which took place in Malaga, Spain, during July 2–4, 2024. The 155 full papers and 70 short papers included in these proceedings were carefully reviewed and selected from 430 submissions. They were organized in topical sections as follows: Part I: ICCS 2024 Main Track Full Papers; Part II: ICCS 2024 Main Track Full Papers; Part III: ICCS 2024 Main Track Short Papers; Advances in High-Performance Computational Earth Sciences: Numerical Methods, Frameworks and Applications; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Part IV: Biomedical and Bioinformatics Challenges for Computer Science; Computational Health; Part V: Computational Optimization, Modelling, and Simulation; Generative AI and Large Language Models (LLMs) in Advancing Computational Medicine; Machine Learning and Data Assimilation for Dynamical Systems; Multiscale Modelling and Simulation; Part VI: Network Models and Analysis: From Foundations to Artificial Intelligence; Numerical Algorithms and Computer Arithmetic for Computational Science; Quantum Computing; Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks, and Artificial Intelligence; Solving Problems with Uncertainties; Teaching Computational Science ."]