Computational Science – ICCS 2021: 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part I

· · · ·
· Springer Nature
電子書籍
797
ページ
評価とレビューは確認済みではありません 詳細

この電子書籍について

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.


この電子書籍を評価する

ご感想をお聞かせください。

読書情報

スマートフォンとタブレット
AndroidiPad / iPhone 用の Google Play ブックス アプリをインストールしてください。このアプリがアカウントと自動的に同期するため、どこでもオンラインやオフラインで読むことができます。
ノートパソコンとデスクトップ パソコン
Google Play で購入したオーディブックは、パソコンのウェブブラウザで再生できます。
電子書籍リーダーなどのデバイス
Kobo 電子書籍リーダーなどの E Ink デバイスで読むには、ファイルをダウンロードしてデバイスに転送する必要があります。サポートされている電子書籍リーダーにファイルを転送する方法について詳しくは、ヘルプセンターをご覧ください。

Maciej Paszynski のその他の書籍

類似の電子書籍