Conferences and Meetings on Scientific Computing

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Particle Physicists study matter at is smallest scale and try to reveal how the physical world is constructed from elementary particles and the forces acting between them. The well-known proton, neutron and similar particles are composed of quarks. Atoms also contain electrons; together with the two heavier versions of the electron (the muon and tau), each with its associated neutrino, they make up the group of leptons. Another ingredient of the catalogue of particles is the Higgs boson, discovered at the CERN Lab's Large Hadron Collider (LHC) in 2012.

Probability and Statistics, Game Theory,    High Energy Physics, Particles and Fields

The Conference is co-sponsored by the Association for Computing Machinery (ACM) and the PASC structuring project and it is managed by the Swiss National Supercomputing Centre (CSCS).

The PASC Conference series is an international and interdisciplinary platform for the exchange of competences in scientific computing and computational science with a strong focus on methods, tools, algorithms, application challenges, and novel techniques and usage of high performance computing.

University of Luxembourg

The aim of the conference ICON STARF is to bring together young researchers and renowned experts in mathematical statistics, signal processing, statistical learning, probability, approximation theory, and all other areas that contribute to the mathematical development of data science. We shall allow time for discussion and stimulate scientific collaboration and exchange among participants. A low registration fee will enable PhD students, post-docs and young researchers to participate in the conference. ICON STARF is part of the SanDAL programme of the ERA Chair which has been awarded to the University of Luxembourg by the European Horizon 2020 initiative.

Probability and Statistics, Game Theory,    Applied Mathematics (in general)

Data science and optimization are closely related. On the one hand, many problems in data science can be solved using optimizers, on the other hand optimization problems stated through classical models such as those from mathematical programming cannot be considered independent of historical data. Examples are ample: Machine Learning (ML) often relies on optimization techniques such as linear or integer programming; reasoning systems have been applied to constrained pattern and sequence mining tasks; a parallel development of metaheuristic approaches has taken place in the domains of data mining and machine learning; methods aimed at high level combinatorial optimization have been shown to strongly profit from configuration, algorithm selection and tuning tools building on historical data; ML models can be embedded in combinatorial optimization problems to address hard-to-model systems, or for validation of the ML model itself; “predict, then optimize” scenarios can be dealt with in an integrated fashion to improve considerably the solution quality.

Modeling and Simulation,    Neural Networks and Artificial Intelligence, Machine Learning

Casa Matemática Oaxaca (CMO) in Mexico, and the Banff International Research Station for Mathematical Innovation and Discovery (BIRS) in Banff, Canada

The development of high-performance simulation tools for real-life problems involving fluid flows and related transport phenomena frequently requires the use of numerical methods that are high-order accurate in space and time and preserve important physical properties of certain quantities of interest (such as nonnegativity of the water height in shallow water equation models). In recent years, the state of the art in the development of such schemes was significantly advanced by a breakthrough in theoretical analysis which has provided illuminating insights into the properties of existing methods and new design criteria for the development of improved solution strategies. However, many theoretical aspects of physics-aware discretization techniques still require further exploration or have not been put into practice so far. The purpose of this workshop is to bridge the gap between the mathematical theory and engineering practice.

Courses and Events for Math Students and Early Career Researchers,    Modeling and Simulation

Institute for Pure and Applied Mathematics (IPAM), UCLA

The aim of this program is to connect efforts of the mathematical and physical sciences communities to address the latest advances and new challenges on the understanding of multi-messenger astronomy. The IPAM program will comprise four workshops, each addressing a different topic: the generation of catalogs of waveform templates; the discussion of the mathematical modeling of the equations governing strong relativistic systems; parameter estimation of astrophysical sources of gravitational waves; and the state of the art of big data and deep learning techniques for GW data analysis.

Astronomy, Astrophysics and Cosmology,    High Energy Physics, Particles and Fields

Institute for Pure and Applied Mathematics (IPAM), Los Angeles, CA

Part of the Long Program Mathematical and Computational Challenges in the Era of Gravitational Wave Astronomy

The Society of Exploration Geophysicists of Japan (SEGJ)

Road ahead for geophysics in digital society: Acquisition, processing, and interpretation

Geophysics and Geology,    Multidisciplinary or General Events in Informatics

Institute for Pure and Applied Mathematics (IPAM).

Part of the Long Program Mathematical and Computational Challenges in the Era of Gravitational Wave Astronomy

CIRM – Centre International de Rencontres Mathématiques

The central aim of this conference is to explore multi-disciplinary research. It focuses on Quantum Computing and Quantum Simulations, Exa-scale Computing and its Applications, Metaheuristics in Science and Technology, Numerical Linear Algebraic Methods for Large Scale Scientific Computing, and Quantum Communications and Quantum Cryptography for Scientists, Researchers and Engineers. Its impact is already being felt in many areas of Sciences and Engineering disciplines. Current research in computational science requires multi-disciplinary knowledge, not only in sciences and engineering but also in technologies of computing. This conference offers academic researchers, developers and practitioners an opportunity to discuss various aspects of computational science and engineering related computational methods and problems solving techniques for science and engineering research.

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The previous editions of ICCDA were held in Florida Polytechnic University, Lakeland, Northern Illinois University (NIU) DeKalb, University of Hawaii Maui College, Kahului, Silicon Valley, USA. ICCDA 2022 conference will be located in Sanya, China during Feb. 18-20, 2021.

Phone: [+86 138 8010 4517];     Email: Nazal@sina.com

Mathematical, probabilistic and statistical models and theories; Machine learning theories, models and systems; Knowledge discovery theories, models and systems; Manifold and metric learning; Deep learning; Scalable analysis and learning; Non-iidness learning; Heterogeneous data/information integration; Data pre-processing, sampling and reduction; Dimensionality reduction; Feature selection, transformation and construction; Large scale optimization; High performance computing for data analytics; Architecture, management and process for data science;

The SIAM Conference on Parallel Processing for Scientific Computing is sponsored by the SIAM Activity Group on Supercomputing.

The SAMO conference is devoted to advances in research on sensitivity analysis methods and their interdisciplinary applications. The SAMO conferences are held every three years. The aim of the SAMO conferences is to bring together users of sensitivity analysis in all disciplines of science. Sensitivity analysis methods are powerful tools in physics, operations research, chemistry, biology, engineering, environmental science, nuclear and industrial safety, economics and finance.

Probability and Statistics, Game Theory,    Modeling and Simulation

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Courses and Events for Math Students and Early Career Researchers,    Algebra

Computational Physics and Numerical Simulation,    Applied Mathematics (in general)