Computational Sciences and Artificial Intelligence in Industry
- Length: 290 pages
- Edition: 1
- Language: English
- Publisher: Springer
- Publication Date: 2021-08-20
- ISBN-10: 3030707865
- ISBN-13: 9783030707866
- Sales Rank: #0 (See Top 100 Books)
This book is addressed to young researchers and engineers in the fields of Computational Science and Artificial Intelligence, ranging from innovative computational methods to digital machine learning tools and their coupling used for solving challenging industrial and societal problems.This book provides the latest knowledge from jointly academic and industries experts in Computational Science and Artificial Intelligence fields for exploring possibilities and identifying challenges of applying Computational Sciences and AI methods and tools in industrial and societal sectors.
Preface Contents Contributors Part I Overview Co-development of Methodology, Applications, and Hardware in Computational Science and Artificial Intelligence 1 Simultaneous Development of Hardware and Software 2 Co-development Potential of Computational Science and Artificial Intelligence 3 Challenges in the Contemporary Field of Scientific Computing and Artificial Intelligence 4 Conclusions References Part II Methodology Novel Strategies for Data-Driven Evolutionary Optimization 1 Introduction 2 Algorithms 2.1 BioGP 2.2 EvoNN 2.3 EvoDN2 2.4 cRVEA 3 Test Functions 4 Applications in Materials Research 5 Concluding Remarks References Artificial Intelligence and Computational Science 1 Classical Method of Scientific Knowledge 2 Genetic Algorithms 3 AI and New Methods of Scientific Analysis 4 Open Problems and Development Prospect 5 Conclusions References Supervised Learning and Applied Mathematics 1 Introduction 2 Applied Mathematics for Supervised Learning 3 Supervised Learning for Applied Mathematics 4 Illustration: Calibration of the Heston Model 4.1 Monte Carlo Solutions 4.2 Calibration 4.3 Numerical Tests 5 Discussion References Application of the Topological Gradient to Parsimonious Neural Networks 1 Introduction 2 Parsimonious Neural Networks 2.1 Topological Optimization Methods 2.2 Adaptation for Compression 3 Numerical Examples 3.1 NeurEco 3.2 Image Compression References Generation of Error Indicators for Partial Differential Equations by Machine Learning Methods 1 Introduction 2 Indicators of Computational Errors 2.1 Adaptive Numerical Methods and Error Indicators 2.2 Accuracy of Error Indicators 2.3 A Class of Boundary Value Problems 3 Network Error Indicator 3.1 Local Networks 4 Examples 5 Conclusions References Newton Method for Minimal Learning Machine 1 Introduction 2 MLM 2.1 Formulae 2.2 MLM Using the Newton Method 3 Experiments 3.1 Experimental Setup 3.2 Results 4 Discussion 5 Conclusions References Limited Memory Bundle Method for Clusterwise Linear Regression 1 Introduction 2 Clusterwise Linear Regression 3 LMBM-CLR 4 Numerical Experiments 4.1 Results on Small Data Sets with Known Solution 4.2 Results on Large Real-World Data Sets 5 Conclusions References Improving Clustering and Cluster Validation with Missing Data Using Distance Estimation Methods 1 Introduction 2 Methods 3 Experiments and Results 4 Discussion 5 Conclusions References Part III Medical Applications Kubelka–Munk Model and Stochastic Model Comparison in Skin Physical Parameter Retrieval 1 Introduction 2 Materials and Methods 2.1 Stochastic Model 2.2 Kubelka–Munk Model 2.3 Convolutional Neural Network 2.4 Model Inversion 3 Results and Discussion 3.1 Retrieval Results 3.2 Model Comparison 3.3 Discussion 4 Conclusion References Unsupervised Numerical Characterization in Determining the Borders of Malignant Skin Tumors from Spectral Imagery 1 Introduction 2 Mathematical Model of the Human Skin 3 Spectral Imaging 4 Computational Methods 4.1 Modified Standard Normal Variate Correction Algorithm 4.2 Linear Unmixing 4.3 Linear Unmixing of Estimated Single Scattering Albedo 4.4 Closed-Form Chromophore Specific Approximation for Estimated Single Scattering Albedo 5 Results 5.1 Simulated Spectral Cube 5.2 Basal Cell Carcinoma 5.3 In-situ Melanoma 6 Discussion 7 Conclusion References Validation of Knee KL-classifying Deep Neural Network with Finnish Patient Data 1 Introduction 2 Materials and Methods 3 Results 4 Discussion and Summary References Predicting Future Overweight and Obesity from Childhood Growth Data: A Case Study 1 Introduction 2 Materials and Methods 2.1 Data 2.2 Model Fitting 2.3 Performance Evaluation 3 Results 4 Discussion 5 Conclusion References Predicting Overweight and Obesity in Later Life from Childhood Data: A Review of Predictive Modeling Approaches 1 Introduction 2 Materials and Methods 3 Results 3.1 Surveys, Literature Reviews, and Meta-analyses 3.2 Predictive Modeling Approaches 4 Conclusions 5 Summary Points References Part IV Industrial and Technological Applications Applications of Industrial IoT and WSNs in O&M Programmes for Offshore Wind Farms 1 Introduction 1.1 Background and Overview 1.2 Remote Monitoring in Offshore Environments 2 Background—Practical Applications of WSNs in Offshore Environments 2.1 Structural and Condition Monitoring 2.2 WSNs for Environmental Monitoring 3 Technology Requirements and Challenges to Address 3.1 Power Requirements 3.2 Topology 3.3 Cyber Security 3.4 Machine Learning and Scientific Computing: Algorithms for Decision Systems 4 Case Study 5 Suggestions and Guidelines 6 Conclusions References Combined Model Order Reduction Techniques and Artificial Neural Network for Data Assimilation and Damage Detection in Structures 1 Introduction 2 Dataset Construction Through a Parametric Model Order Reduction (pMOR) Technique 3 Fully Convolutional Network 4 Numerical Benchmark 5 Conclusion References Using Wave Propagation Simulations and Convolutional Neural Networks to Retrieve Thin Film Thickness from Hyperspectral Images 1 Introduction 2 Materials and Methods 2.1 1D Wave Simulations with Discrete Exterior Calculus 2.2 Convolutional Neural Network 2.3 Measured Reference Samples 2.4 Hyperspectral Imaging 3 Results 4 Discussion and Conclusion References
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