Preface
Acknowledgements
Contents
About the Editors
Introduction to Biomimetics, Modelling and Analysis
1 Definition and Scope of Biomimetics
2 Historical Background of Biomimetics
3 Key Principles and Approaches in Biomimetics
4 Modeling and Analysis in Biomimetics
5 Applications of Biomimetics
5.1 Materials Science and Engineering
5.2 Robotics and Automation
5.3 Medicine and Healthcare
5.4 Energy and Sustainable Design
6 Challenges and Future Directions
6.1 Challenges Related to Biomimetics
6.2 Future of Biomimetics
7 Conclusion
References
High Performance Computing and Its Application in Computational Biomimetics
1 Definition and Overview
1.1 High-Performance Computing as a System
1.2 High Performance Computing: Uses and Benefits
2 Evolution of HPC
3 Characteristics and Components of HPC Systems
3.1 Characteristics of High Performance Computing
3.2 Components of High Performance Computing
4 Importance of HPC in Scientific Research and Engineering
4.1 Simulation and Modeling
4.2 Big Data Analytics
4.3 Optimization and Design
4.4 Data-Intensive Research
5 HPC Technologies and Architectures
5.1 Parallel Computing
5.2 Distributed Computing
5.3 Grid Computing
5.4 Cluster Computing
5.5 Supercomputing
5.6 Accelerators and Co-processors (e.g., GPUs, FPGAs)
6 Computational Biomimetics
7 Importance of Computational Biomimetics
7.1 Advancing Scientific Understanding
7.2 Innovation in Engineering and Technology
7.3 Sustainability and Environmental Conservation
7.4 Interdisciplinary Collaboration
8 Role of HPC in Computational Biomimetics
8.1 Simulation and Modeling
8.2 Data Analysis and Processing
8.3 Optimization and Design
8.4 Visualization and Virtual Reality
9 HPC Applications in Computational Biomimetics
10 Challenges and Future Directions
10.1 Scalability and Performance Optimization
10.2 Big Data and Data Management
10.3 Energy Efficiency and Sustainability
10.4 Integration of HPC with Machine Learning and Artificial Intelligence
10.5 Cloud Computing and HPC
11 Case Studies: HPC in Computational Biomimetics
11.1 Case Study 1: Modeling the Flight of Birds/Insects for Aircraft Design
11.2 Case Study 2: Simulating Biomolecular Interactions for Drug Discovery
12 Conclusions
References
Bio-inspired Computing and Associated Algorithms
1 Introduction to Bio-inspired Computing
1.1 Definition and Overview
1.2 Inspiration from Biological Systems
1.3 Relationship Between Bio-inspired Computing and Artificial Intelligence
2 Biological Inspiration for Computing
2.1 Neural Networks and Artificial Neurons
2.2 Evolutionary Algorithms and Genetic Algorithms
2.3 Swarm Intelligence and Ant Colony Optimization
2.4 Cellular Automata and Self-organization
2.5 DNA Computing and Molecular Computing
3 Neural Networks and Deep Learning
3.1 Introduction to Neural Networks
3.2 Perceptrons and Multilayer Neural Networks
3.3 Training Algorithms (E.G., Backpropagation)
3.4 Convolutional Neural Networks (CNNs)
3.5 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
3.6 Deep Learning Applications and Success Stories
4 Evolutionary Algorithms and Genetic Algorithms
4.1 Principles of Evolutionary Computation
4.2 Genetic Algorithms: Chromosomes, Genes, and Fitness Evaluation
4.3 Selection, Crossover, and Mutation Operators
4.4 Genetic Programming and Evolutionary Strategies
4.5 Applications of Evolutionary Algorithms
5 Metaheuristic Algorithms
5.1 Evolutionary Algorithms
5.2 Swarm Intelligence
5.3 Plant-Based Algorithms
5.4 Human-Based
6 Neuroevolution: Combining Neural Networks and Evolutionary Algorithms
7 Bionic Optimization: Integrating Biological Principles into Optimization Algorithms
8 Bio-inspired Algorithms for Optimization, Scheduling, and Pattern Recognition
9 Applications
10 Conclusions
References
Cloud Computing Infrastructure, Platforms, and Software for Scientific Research
1 Introduction to Cloud Computing
1.1 Definition and Overview
1.2 Evolution of Cloud Computing
1.3 Characteristics and Benefits of Cloud Computing
1.4 Role of Cloud Computing in Research
2 Amazon Web Services (AWS)
2.1 Overview of AWS Services and Offerings
2.2 AWS Infrastructure and Data Centers
2.3 Google Cloud Platform (GCP)
2.4 Microsoft Azure
2.5 Other Cloud Computing Providers and Their Research Capabilities
3 Cloud Computing Platforms for Research
3.1 Virtual Machines (VMs) and Infrastructure as a Service (IaaS)
3.2 Containers and Container Orchestration (E.G., Kubernetes)
3.3 Platform as a Service (PaaS) and Serverless Computing
3.4 Big Data and Analytics Platforms in the Cloud
3.5 AI and Machine Learning Platforms in the Cloud
3.6 High Performance Computing (HPC) and Cloud-Based Clusters
4 Cloud Computing Software for Research
4.1 Data Storage and Database Services
4.2 Data Processing and Analytics Tools
4.3 Collaboration and Workflow Management Tools
4.4 Data Visualization and Reporting Tools
4.5 Machine Learning and AI Frameworks
4.6 Simulation and Modeling Software
4.7 Cloud Based IOT Applications like for Example Smart Cities
5 Security, Privacy, and Compliance in Cloud Computing for Research
5.1 Cloud Security Best Practices
5.2 Data Privacy and Protection Considerations
5.3 Compliance with Regulatory Requirements (E.G., GDPR, HIPAA)
5.4 Data Governance and Access Control
6 Challenges and Future Directions
6.1 Data Transfer and Bandwidth Limitations
6.2 Interoperability and Vendor Lock-In
6.3 Integration of Cloud Computing with On-Premises Infrastructure
6.4 Advances in Cloud Computing Technologies for Research
6.5 Ethical Considerations in Cloud-Based Research:
7 Conclusions
References
Expansion of AI and ML Breakthroughs in HPC with Shift to Edge Computing in Remote Environments
1 Artificial IntelligenceâAÂ Comprehensive Approach
2 Machine Learning
3 Neural Networks
4 Deep Neural Network
5 High Performance Computing
6 Edge Computing
7 The Convergence of HPC, AI, and ML
7.1 HPC's Historical Significance
7.2 The AI and ML Revolution
7.3 HPC Meets AI/ML
8 Challenges in Remote Environments
8.1 Remote Environments Defined
8.2 Challenges Faced
9 Integration of AI and ML with Edge Computing in Remote Environments
9.1 Customized Hardware
9.2 Distributed AI/ML Models
9.3 Anomaly Detection
References
Role of Distributed Computing in Biology Research Field and Its Challenges
1 Introduction
1.1 Biology: Experimental Biology Versus Bioinformatics
1.2 From Conventional to the âModernâ Experimental Biology
2 High-Performance Computing, Parallel, and Distributed Computing
3 Role of Distributed Computing Application in the Biology Research Field
4 Challenges and Limitations of Distributed Computing Application in a Biology Research Field
5 Future Directions of Distributed Computing Application
6 Conclusion
References
HPC Based High-Speed Networks, ARM Processor Architecture and Their Configurations
1 Introduction
2 High-Performance Computing (HPC) Platforms
2.1 Key Features of HPC Platforms
3 High-Speed Networks
3.1 Key Features of High-Speed Networks
3.2 ARM Processor Architecture
3.3 Key Features of ARM Architecture
3.4 ARM in HPC
4 Configurations for ARM-Based HPC
5 Conclusion
References
High-Performance Computing Based Operating Systems, Software Dependencies and IoT Integration
1 Introduction
1.1 Background
1.2 Key Components and Architecture
2 Role of Jetson in High-Performance Computing
3 Operating Systems for High-Performance Computing
3.1 Linux in HPC: Advantages and Adaptability
3.2 Nvidia Jetson Supported Operating Systems
3.3 Selection Criteria for Choosing an OS
4 Software Dependencies in HPC
4.1 Definition and Significance
4.2 Libraries and Frameworks for HPC
4.3 CUDA and CuDNN: Nvidia's GPU Computing Technologies
4.4 TensorRT: Deep Learning Inference Optimizer
4.5 Other Software Dependencies for IoT Integration
5 Integration of Nvidia Jetson and IoT
5.1 Internet of Things (IoT)
5.2 IoT Applications in High-Performance Computing
5.3 Nvidia Jetson for IoT Edge Computing
5.4 Challenges and Considerations for IoT Integration
6 Optimizing Software Dependencies for HPC with Nvidia Jetson and IoT
6.1 Performance Optimization Techniques
6.2 Memory Management and GPU Utilization
6.3 Power and Thermal Management
6.4 Code Profiling and Debugging
6.5 Monitoring and Analytics for IoT Integration
7 Some Case Studies: HPC with Nvidia Jetson and IoT Integration
7.1 Case Study 1: Real-Time Image Processing for the Internet of Things
7.2 Case Study 3: Edge AI for Industrial Automation
8 Future Trends and Challenges
8.1 Emerging Technologies in HPC and IoT
8.2 Challenges in Scaling HPC with IoT Integration
8.3 Potential Solutions and Research Directions
9 Conclusion
References
GPU and ASIC as a Boost for High Performance Computing
1 Introduction
2 GPU and ASIC Acceleration
3 Parallel Processing Capabilities of GPUs
4 GPU Architecture and HPC Performance
5 GPGPU Programming Frameworks: CUDA and OpenCL
6 Heterogeneous Computing: CPU-GPU Collaboration
7 ASICs and Custom Hardware Design
8 Advantages of ASICs in HPC Performance
9 Comparison of GPUs and ASICs in HPC Applications
10 Integration and Coexistence of GPUs and ASICs in HPC Systems
11 Conclusion
References
Biomimetic Modeling and Analysis Using Modern Architecture Frameworks like CUDA
1 Introduction
1.1 Background
2 Biomimetic Modeling
2.1 Definition of Biomimetic Modeling
2.2 The Relevance of Biomimetic Modeling
2.3 Challenges in Biomimetic Modeling
3 CUDA Architecture
3.1 Overview of CUDA
3.2 CUDA in Scientific Computing
3.3 CUDA in Biomimetic Modeling
4 Application of CUDA in Biomimetic Modeling
4.1 Molecular Dynamics Simulation
4.2 Neural Network Training
4.3 Biomechanics and Fluid Dynamics
4.4 Evolutionary Algorithms
5 Recent Case Study
6 Challenges and Future Prospects in the Integration of CUDA in Biomimetic Modeling
6.1 Challenges
6.2 Future Prospects
7 Conclusion
References
Unsteady Flow Topology Around an Insect-Inspired Flapping Wing Pico Aerial Vehicle
1 Introduction
2 Background and Methodology
3 Results and Discussion
4 Conclusion
References
Machine Learning Based Dynamic Mode Decomposition of Vector Flow Field Around Mosquito-Inspired Flapping Wing
1 Introduction
2 Methodology
3 Results and Discussion
4 Conclusion
References
Application of Cuckoo Search Algorithm in Bio-inspired Computing Using HPC Platform
1 Introduction
2 Cuckoo Search Algorithm
2.1 CSA Modeling
2.2 Pseudocode Implementation of the Cuckoo Search Algorithm
3 High-Performance Computing (HPC) Platform
3.1 Parallelization of Cuckoo Search
3.2 Python Syntax for Parallel Cuckoo Search
4 Case Studies
5 Conclusion
References
Application of Machine Learning and Deep Learning in High Performance Computing
1 Machine Learning: Concepts and Techniques
2 Deep Learning: Neural Networks and Architectures
3 Parallelism in DL and Distributed Computing
4 Training and Inference in ML and DL
5 Convergence of ML/DL and HPC
6 Motivation for Integrating ML/DL with HPC
7 Benefits and Challenges of ML/DL in HPC
8 Advances in ML/DL for HPC
9 Hardware and Software Architectures
10 Conclusion
References
The Future of High Performance Computing in Biomimetics and Some Challenges
1 Introduction
2 Computational Strength
3 Memory and Storage
4 The Function of Artificial Intelligence
5 The Internet of Things IoT and Smart Cities
6 Role of Quantum Computing for Structural Biology
7 The Future of HPC in Biomimetics
8 Challenges in Harnessing HPC for Biomimetics
References