SIAM Conference on Computational Science and Engineering (CSE19)

Страна: США

Город: Washington

Тезисы до: 22.08.2018

Даты: 25.02.19 — 01.03.19

Е-мейл Оргкомитета:

Организаторы: Society for Industrial and Applied Mathematics


Computational Science and Engineering (CSE) complement theory and experiment as a critical component of scientific discovery. CSE is indispensable for leading edge investigation and engineering design in a vast number of industrial sectors, including for example, aerospace, automotive, biological, chemical, and semiconductor technologies that all rely increasingly on advanced modeling and simulation. CSE also contributes to policy and decisions relating to human health, resources, transportation, and defense. Finally, in many new areas such as medicine, the life sciences, management and marketing (e.g., data and stream mining), and finance, techniques and algorithms from CSE are of growing importance.

CSE is naturally interdisciplinary. Its goals concern understanding and analyzing complex systems, predicting their behavior, and optimizing processes and designs. CSE thus grows out of physical applications, while depending on computer architecture and software, and having at its core powerful algorithms. At the frontiers of CSE remain many open problems and challenges, including the verification, validation, and uncertainty quantification of computational models; the analysis and assimilation of large and complex data sets, including techniques for visualization and animation; and the design of flexible CSE software.

The SIAM CSE conference seeks to enable in-depth technical discussions on a wide variety of major computational efforts on large-scale problems in science and engineering, foster the interdisciplinary culture required to meet these large-scale challenges, and promote the training of the next generation of computational scientists.

Included Themes

  • Computational science and machine learning
  • Statistical modeling, methods, and computation
  • Multiscale, multiphysics, and multilevel methods
  • High performance software: packages and design
  • Algorithms at extreme scales
  • Tensor Computations
  • High-order methods, novel discretizations, and scalable solvers
  • Data science, analytics, and visualization
  • Applications in science, engineering, and industry
  • Biological and biomedical computations
  • Scientific simulation and uncertainty
  • Numerical optimization: methods and applications
  • Reduced order modeling
  • Emerging trends in CS&E education and training

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