
Computational Science - ICCS 2024
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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
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Content
- Intro
- Preface
- Organization
- Contents - Part I
- ICCS 2024 Main Track Full Papers
- Effects of Wind on Forward and Turning Flight of Flying Cars Using Computational Fluid Dynamics
- 1 Introduction
- 2 Numerical Approach
- 2.1 Fundamental Equation of Fluid
- 2.2 Fundamental Equation of Rigid Body
- 2.3 Moving Computational Domain Approach
- 2.4 Multi-axis Sliding Mesh Approach
- 3 Simulation Summary
- 3.1 Flight Simulation Conditions
- 3.2 Computation Model
- 3.3 Calculation Conditions
- 3.4 Attitude Control
- 4 Simulation Results
- 4.1 Forward Flight Simulation
- 4.2 Acceleration Turning Flight
- 4.3 Constant Velocity Turning Flight
- 5 Conclusions
- References
- DP-PINN: A Dual-Phase Training Scheme for Improving the Performance of Physics-Informed Neural Networks
- 1 Introduction
- 2 Related Work
- 3 Method
- 3.1 Overview of Standard PINN and Motivation of DP-PINN
- 3.2 DP-PINN
- 3.3 Sampling Methods
- 4 Results
- 4.1 Allen-Cahn Equation
- 4.2 1D Viscous Burger's Equation
- 5 Conclusion and Future Works
- References
- Operator Entanglement Growth Quantifies Complexity of Cellular Automata
- 1 Introduction
- 2 A Matrix Product Operator for Elementary Cellular Automata
- 3 Operator Space Entanglement Entropy Growth
- 3.1 Type I Rules: Quick Convergence to Constant Values
- 3.2 Type II Rules: Long Transients and Information Transfer
- 3.3 Type III Rules: Chaotic CAs
- 3.4 Type IV Rules: Domain Walls, Lifeforms and Complexity
- 3.5 Parallel with Quantum Systems
- 4 Conclusion
- References
- HarVI: Real-Time Intervention Planning for Coronary Artery Disease Using Machine Learning
- 1 Introduction
- 2 Methods
- 2.1 Personalized Hemodynamic Analysis Using Reduced-Order Models
- 2.2 Integrating with Harvis and Editing 3D Stenosis Geometry
- 2.3 Establishing a Real-Time Prediction Model of Post-intervention Hemodynamics Using Machine Learning
- 2.4 Experimental Protocol to Establish and Validate HarVI
- 3 Results and Discussion
- 3.1 Few Samples Are Needed to Capture Post-intervention Fractional Flow Reserve Accurately
- 3.2 Post-intervention Fractional Flow Reserve Predicted Using HarVI Agrees with 1D Ground-Truths
- 3.3 End-to-End Turnaround Time to Enable Intervention Planning Within One Working Day
- 4 Conclusion
- References
- Krylov Solvers for Interior Point Methods with Applications in Radiation Therapy and Support Vector Machines
- 1 Introduction
- 2 Background
- 2.1 Interior Point Methods
- 2.2 Optimization Problems in Radiation Therapy and SVMs
- 3 Prototype Interior Point Method
- 3.1 KKT System Formulation
- 3.2 IPM Implementation
- 4 Experimental Setup
- 5 Results
- 5.1 Krylov Solver Convergence
- 5.2 IPM Solver Convergence
- 5.3 Numerical Stability and Conditioning of KKT System
- 5.4 Performance Analysis
- 6 Related Work
- 7 Conclusions and Future Work
- References
- From Fine-Grained to Refined: APT Malware Knowledge Graph Construction and Attribution Analysis Driven by Multi-stage Graph Computation
- 1 Introduction
- 2 Related Work
- 3 Proposed Method
- 3.1 APT Malware Ontology Mode
- 3.2 Multistage Graph Clustering and Graph Representation Optimization
- 3.3 Threat Actor Groupal Attribution Based on Meta-Path Graph Embedding
- 4 Evaluation and Discussion
- 4.1 Discussion 1: Whether the Key Information of APT Malware Knowledge Graph Attribution Is Reduced After Refining
- 4.2 Discussion 2: What Is the Effect of Refined APT Malware Knowledge Graph Attribution Analysis
- 4.3 Discussion 3: How Meaningful Is Our Research in Comparison with Similar Studies in the Field of APT Malware Analysis
- 5 Conclusion
- References
- Flow Field Analysis in Vortex Ring State Using Small Diameter Rotor by Descent Simulation
- 1 Introduction
- 2 Numerical Approach
- 2.1 Governing Equations
- 2.2 Unstructured Moving-Grid Finite-Volume Method
- 2.3 Moving Computational Domain Method
- 2.4 Hovering Induced Velocity
- 3 Descent Simulation of Rotor
- 3.1 Computational Mesh
- 3.2 Computational Mesh
- 4 Numerical Simulation Results
- 4.1 Simple Vertical Descent
- 4.2 Oblique Descent Simulation (Angle of Descent 26.6°)
- 5 Conclusions
- References
- Toward Real-Time Solar Content-Based Image Retrieval
- 1 Introduction
- 2 Related Works
- 3 Proposed Method for Solar Image Hashing
- 3.1 Calculating Solar Image Descriptor
- 3.2 Hash Generation
- 3.3 Retrieval
- 4 Experimental Results
- 5 Conclusions
- References
- Velocity Temporal Shape Affects Simulated Flow in Left Coronary Arteries
- 1 Introduction
- 2 Methods
- 2.1 Quantifying and Varying Temporal Waveform Shape
- 2.2 1D Simulation Approach
- 2.3 3D Simulation Approach
- 2.4 Image-Derived Coronary Geometries
- 2.5 Numerical Experimental Protocol
- 2.6 Calculating Hemodynamic Metrics
- 2.7 Statistical Tests
- 3 Results
- 3.1 1D Quantification of Wall Shear Stress
- 3.2 A Subset of Perturbed Points of Interest Resulted in Significant 3D Simulation Changes
- 3.3 Strong Agreement Between 1D and 3D Wall Shear Stress
- 3.4 Correlation Between Metrics and Inlet Waveform Metrics
- 4 Discussion
- 4.1 1D Linear Changes in Wall Shear Stress
- 4.2 Three Points of Interest Cause 3D Statistically Significant OSI Differences
- 4.3 Inlet Waveforms Are Not Enough to Predict All Metrics
- 4.4 Limitations
- 4.5 Clinical and Research Implications
- 5 Conclusion
- References
- Simulating, Visualizing and Playing with de Sitter and Anti de Sitter Spacetime
- 1 Introduction
- 2 Background
- 3 Preliminaries
- 4 Simulation of the Anti-de Sitter Spacetime
- 5 Simulation of the de Sitter Spacetime
- 6 Visualizations and Insights
- 7 Further Work
- References
- Enhancing the Realism of Wildfire Simulation Using Composite Bézier Curves
- 1 Introduction
- 2 Forest Fire Spread Modelling
- 3 Methodology
- 3.1 Composite Bézier Curves
- 4 Experimental Study and Results
- 4.1 Ideal Cases
- 4.2 Real Case
- 5 Conclusions
- References
- Learning Mesh Geometry Prediction
- 1 Introduction
- 2 Related Work
- 3 Compression Method
- 3.1 Input Features
- 3.2 Optimization
- 3.3 Uncertainty Prediction
- 4 Experimental Results
- 5 Conclusions
- References
- Data-Efficient Knowledge Distillation with Teacher Assistant-Based Dynamic Objective Alignment
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 3.1 Preliminary
- 3.2 Data Selection (DS)
- 3.3 Teacher Assistant (TA)
- 3.4 Dynamic Objective Alignment (DOA)
- 3.5 Total Loss
- 4 Experiments
- 4.1 Datasets
- 4.2 Baselines
- 4.3 Implementation Details
- 4.4 Evaluation Metrics
- 4.5 Performance Comparison
- 4.6 Ablation Study
- 4.7 Model Analysis
- 5 Conclusion
- References
- MPI4All: Universal Binding Generation for MPI Parallel Programming
- 1 Introduction
- 2 Related Work
- 3 MPI4All
- 3.1 Parser
- 3.2 Generator
- 4 Experimental Evaluation
- 4.1 Java
- 4.2 Go
- 5 Conclusions
- References
- Time Series Predictions Based on PCA and LSTM Networks: A Framework for Predicting Brownian Rotary Diffusion of Cellulose Nanofibrils
- 1 Introduction
- 2 Related Work
- 3 Methods
- 3.1 Dimensionality Reduction
- 3.2 Long Short-Term Memory
- 4 Input Data and Experiment Description
- 4.1 LSTM Architecture
- 5 Results
- 6 Discussion and Conclusion
- References
- Cost-Effective Defense Timing Selection for Moving Target Defense in Satellite Computing Systems
- 1 Introduction
- 2 Related Work
- 2.1 Time-Driven MTD
- 2.2 Event-Driven MTD
- 2.3 Hybrid-Driven MTD
- 3 System and Threat Model
- 4 Markov Game
- 5 Defense Timing Decision
- 5.1 Decision Equations of Players
- 5.2 Solutions of Decision Equations
- 6 Experiments
- 6.1 Experimental Environment and Settings
- 6.2 Performance Evaluation
- 6.3 Defense Effect Evaluation
- 7 Conclusion
- References
- Energy- and Resource-Aware Graph-Based Microservices Placement in the Cloud-Fog-Edge Continuum
- 1 Introduction
- 2 Background
- 3 Model Formulation
- 3.1 Network Infrastructure
- 3.2 Microservices Applications and Function Paths
- 3.3 Energy Efficiency Placement Problem
- 3.4 Minimum Execution Time of an MFP
- 4 Proposed Solution for Energy Efficiency Microservices Placement
- 4.1 Community Based Selection
- 4.2 Node Inside Community Selection
- 5 Evaluation and Results
- 5.1 Experimental Setup
- 5.2 Results Analysis
- 6 Conclusion and Future Work
- References
- Fast and Layout-Oblivious Tensor-Matrix Multiplication with BLAS
- 1 Introduction
- 2 Related Work
- 3 Background
- 4 Algorithm Design
- 4.1 Baseline Algorithm with Contiguous Memory Access
- 4.2 BLAS-Based Algorithms with Tensor Slices
- 4.3 BLAS-Based Algorithms with Subtensors
- 4.4 Parallel BLAS-Based Algorithms
- 5 Experimental Setup
- 6 Results and Discussion
- 7 Conclusion and Future Work
- References
- Interpoint Inception Distance: Gaussian-Free Evaluation of Deep Generative Models
- 1 Introduction
- 2 Related Work
- 3 Interpoint Inception Distance
- 4 Experiments
- 5 Conclusions
- References
- Elliptic-Curve Factorization and Witnesses
- 1 Introduction
- 2 Separating and Nonseparating Witnesses in (Ell, N) Factorization
- 2.1 Notation and Basic Facts Concerning the Arithmetic in E(ZN)
- 2.2 Admissible Numbers and Witnesses Definitions
- 3 Decomposition Witnesses in (Ell,EN) factorization
- 4 Main Results for (Ell, N) Factorization
- 4.1 Separating Witnesses
- 4.2 Nonseparating Witnesses
- 5 Separating Witnesses for Subexponential B and D
- 5.1 Computational Support
- 6 Main Result for (Ell,EN) Factorization
- References
- Data-Driven 3D Shape Completion with Product Units
- 1 Introduction
- 2 Methods
- 2.1 Mathematical Model
- 2.2 Data Sets and Data Acquisition
- 2.3 Data Processing
- 2.4 Network Training
- 2.5 Identify the Missing Parts of the Point Cloud in the Real-World Task
- 3 Results
- 3.1 Synthetic Objects
- 3.2 Real-World Task with Real Objects
- 4 Conclusion and Discussion
- References
- Optimizing BIT1, a Particle-in-Cell Monte Carlo Code, with OpenMP/OpenACC and GPU Acceleration
- 1 Introduction
- 2 Background
- 3 Methodology and Experimental Setup
- 3.1 Hybrid MPI and OpenMP/OpenACC BIT1
- 3.2 Accelerating BIT1 with OpenMP and OpenACC
- 3.3 Experimental Setup
- 4 Performance Results
- 4.1 Hybrid MPI and OpenMP/OpenACC BIT1
- 4.2 Accelerating BIT1 with GPUs
- 5 Related Work
- 6 Discussion and Conclusion
- References
- Brain-Inspired Physics-Informed Neural Networks: Bare-Minimum Neural Architectures for PDE Solvers
- 1 Introduction
- 2 Background
- 3 Brain-Inspired Physics-Informed Neural Networks
- 3.1 Implementation
- 4 Results
- 4.1 Deriving Modular PINN Architectures
- 5 Related Work
- 6 Discussion and Conclusion
- References
- NeRFlame: Flame-Based Conditioning of NeRF for 3D Face Rendering
- 1 Introduction
- 2 Related Works
- 3 NeRFlame: Flame-Based Conditioning of NeRF for 3D Face Rendering
- 3.1 Flame
- 3.2 NeRF
- 3.3 NeRFlame
- 3.4 Controlling NeRF Models to Obtain Face Manipulation
- 4 Experiments
- 4.1 Reconstruction Quality
- 4.2 Mesh Fitting
- 4.3 Face Manipulation
- 4.4 Comparison with MoFaNeRF ch24zhuang2022mofanerf
- 5 Conclusions
- References
- Dynamic Growing and Shrinking of Neural Networks with Monte Carlo Tree Search
- 1 Introduction
- 2 Literature Survey
- 3 The Method
- 3.1 Neural Architecture Design Changes
- 3.2 Monte Carlo Tree Search
- 3.3 Training Scheme - The Complete Algorithm
- 3.4 Complexity Analysis
- 4 Empirical Analysis
- 4.1 Datasets and Empirical Setup
- 4.2 Classification Quality of the New Approach
- 4.3 Parameters of the Method and Their Impact on the Procedure
- 5 Conclusion
- References
- Author Index
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