Computational Science - ICCS 2024

Name: Computational Science - ICCS 2024 | 24th International Conference, Malaga, Spain, July 2-4, 2024, Proceedings, Part I
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24th International Conference, Malaga, Spain, July 2-4, 2024, Proceedings, Part I

Leonardo Franco Clélia de Mulatier Maciej Paszynski Valeria V. Krzhizhanovskaya Jack J. Dongarra Peter M. A. Sloot(Editor)

Springer (Publisher)

Published on 27. June 2024

XXIII, 380 pages

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978-3-031-63749-0 (ISBN)

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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
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Computational Science - ICCS 2024

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