Computational Sciences and Artificial Intelligence in Industry: New Digital Technologies for Solving Future Societal and Economical Challenges

ISBN-13: 9783030707866 / Angielski / Twarda / 2021 / 275 str.

Tero Tuovinen; Jacques Periaux; Pekka Neittaanmaki

Spis treści

• Novel strategies for data-driven evolutionary optimization
• Machine learning using distance-based methods
• Counting cells and predicting immunoscore using gradient boosted convolutional neural networks
• Kubelka-Munk model and stochastic model comparison in skin physical parameter retrieval using neural networks
• A combined approach of neural networks and graphical models in skin cancer inference using spectral imaging
• Using wave propagation simulations and convolutional neural networks to retrieve thin coating’s thickness from hyperspectral images
• Predicting future overweight and obesity from childhood growth data: A case study
• Variable selection under a value acquisition budget
• Stochastic approximation by successive piecewise linearization
• Non-convex robust low-rank matrix recovery
• Neural network learning via successive piecewise linearization
• Learning for scientific computing purposes
• Computational intelligence in design of new nanomaterials
• Modeling flow, reactive transport and geomechanics in porous media
• Physics constrained machine learning for industrial applications
• Parameter and type identification in partial differential equations using deep neural networks
• Stability maximization for layered moving web with total mass constraint
• Similarity solutions for condensation on a non-isothermal vertical plate
• Enhanced topology optimization approach using moving morphable components coupled with NURBS curves
• Combined model order reduction and artificial neural network for data assimilation and damage detection in structures
• Towards the optimization of fuzzy pattern trees by abs - linearization
• Support vector machines in clusterwise linear regression
• A Second-order method with enriched hessian information for composite sparse optimization problems
• Missing value imputation via nonsmooth optimization and clusterwise linear regression
• Parsimonious neural networks
• Nobody can stop advancing artificial intelligence (AI) where developing
• Computational sciences, physics field theories and geometry
• Mini-symposium on ethics in AI
• Essentializing software engineering practices for ethically designing and developing artificial intelligence systems 30 Ethics is important, but how can we implement it? Survey on software developers’ views on AI ethics
• Industrial IoT capabilities in reducing the LCOE of offshore wind energy: A review
• High-Performance data analysis with the Helmholtz Analytics Toolkit (HeAT)
•  Dynamic data-driven application systems based on tensor factorization: learning the physics of model evolution
• Predicting customer experience
• Puhti-AI: Finland’s new AI supercomputer
• Using Artificial Intelligence to Classify Textual Applications for Reporting Purposes  Application of machine learning methods to error control of approximate solutions
• Iterative data selection strategy in offline data-driven evolutionary multiobjective optimization
• On surrogate management in interactive multiobjective building energy system design
• A modified deep neural network for the rapid inversion of geo-physical resistivity measurements
• Using agents for automatic meta-modelling algorithm selection in data-driven multiobjective optimization problems
• Future cooperation between Computational Science and AI in Industrial and Societal Applications - challenges, impact and expectations?
• Artificial Intelligence, Deep Learning and Science Policy in France
• AI and Digital Twin challenges in current EU arenas – in overall and from Finnish perspective
• AI and Data Analytics at a Centre for Scientific Computing
• AI in the field of medical applications

 

Blurb

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.​