Computational Sciences and Artificial Intelligence in Industry: New Digital Technologies for Solving Future Societal and Economical Challenges
Author:   Tero Tuovinen ,  Jacques Periaux ,  Pekka Neittaanmäki
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2022
Volume:   76
ISBN:  

9783030707897


Pages:   275
Publication Date:   21 August 2022
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Our Price $465.72 Quantity:  
Add to Cart

Share |

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


Add your own review!

Overview

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.

Full Product Details

Author:   Tero Tuovinen ,  Jacques Periaux ,  Pekka Neittaanmäki
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2022
Volume:   76
Weight:   0.450kg
ISBN:  

9783030707897


ISBN 10:   303070789
Pages:   275
Publication Date:   21 August 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Chapter 1. Co-development of Methodology, Applications,and Hardware in Computational Science and Artificial Intelligence​.- Chapter 2. Novel Strategies for Data-driven Evolutionary Optimization.- Chapter 3. Artificial Intelligence and Computational Science.- Chapter 4. Supervised Learning and Applied Mathematics.- Chapter 5. Application of the Topological Gradient to Parsimonious Neural Networks.- Chapter 6. Generation of Error Indicators for Partial Differential Equations by Machine Learning Methods.- Chapter 7. Newton Method for Minimal Learning Machine.

Reviews

Author Information

Tab Content 6

Author Website:  

Customer Reviews

Recent Reviews

No review item found!

Add your own review!

Countries Available

All regions
Latest Reading Guide

MRG2025CC

 

Shopping Cart
Your cart is empty
 Shopping cart
Mailing List

 

Sign up now