Computational Science – ICCS 2021: 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part I (1st ed. 2021) (Lecture Notes in Computer Science #12742)

Synopsis

The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.*The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named:Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually.Chapter “Deep Learning Driven Self-adaptive hp Finite Element Method” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Copyright:

2021

Book Details

Book Quality:

Publisher Quality

ISBN-13:

9783030779610

Related ISBNs:

9783030779603

Publisher:

Springer International Publishing

Date of Addition:

07/11/21

Copyrighted By:

Springer Nature Switzerland AG

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Nonfiction, Art and Architecture, Computers and Internet

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.

Edited by:

Maciej Paszynski

Edited by:

Dieter Kranzlmüller

Edited by:

Valeria V. Krzhizhanovskaya

Edited by:

Jack J. Dongarra

Edited by:

Peter M. Sloot