Konferenzen  >  Informatik  >  Wissenschaftliches Rechnen und Datenwissenschaft

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

Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen,    Informations & Wissensmanagement, Big Data Computing

While Full Waveform Inversion (FWI) has made giant strides recently for deep water subsurface imaging, the technology is lagging significantly for land and shallow water applications. Noise — whether ambient or source related — is clearly an issue, especially for the low frequencies that are so critical for successful implementation. Perhaps more problematic is the need to construct an accurate high-resolution model of the near surface. Depending on the terrain, the near surface can have large velocity contrasts, high anisotropy, high attenuation, significant heterogeneities leading to scattering, and strong multiple generators. A proper model of the near surface needs to be possibly viscoelastic and requires a fine computational grid to handle the ultra-low elastic velocities we encounter in the near surface, which may be a challenge for today’s computer capacity. To make matters worse, the near surface is often ill-sampled by cost effective 3D acquisition designs. Despite all these hurdles we are starting to see some beneficial FWI applications to land and shallow water surveys. How important are data conditioning, wavelet estimation, and the choice of the initial model? Do we have to move to elastic FWI? Can ground roll, mud roll, and multiples help rather than hinder the process? Do we need to design our surveys differently? Can we use other complementary data? What do we do with all the scattering noise? What is the role of machine learning (ML)? Is there room for improvement in acquisition? The goal of this workshop is to review the state-of-the art and investigate the next key steps to address some of these questions. Even though FWI is a potentially “complete model” of complex wave propagation, is the problem currently too difficult and too expensive that it needs help in the form of data conditioning and model conditioning? If so, what type of data conditioning and modeling conditioning?

Geophysik und Geologie,    Messtechnik

National Institute for Research and Development of Isotopic and Molecular Technologies INCDTIM Cluj-Napoca

The ICASC 2023 conference continues the series of the former 14 annual events of the Romanian LCG Federation (RO-LCG) on “Grid, Cloud and High-Performance Computing in Science“ (2007-2022). Its title has been changed and the topics have been expanded to offer a more comprehensive coverage of the current evolution in scientific computing.

ICASC 2023 is devoted to recent developments on the application of advanced computing technologies and methods in physics research and related areas. The conference is addressed to specialists with various backgrounds of expertise, such as computer scientists and researchers in numerical methods, developers and users of software applications, quantum computing, providers and beneficiaries of data-intensive services, data center administrators.

Email: icasc@itim-cj.ro

Grid Cloud and HPC computing in research, economy and business, Grid and applications, including eScience and eBusiness, Quantum Computing, Embedded and Pervasive Computing E-infrastructures for collaborative research, National contributions to the computational support of large-scale collaborations, Software and services for distributed computing infrastructures, Numerical methods for physics, Development and optimization of software applications in Grid/Cloud/HPC and Quantum environments, Applications of machine learning methods in scientific research, Monitoring, management and data organization tools, Middleware, resource management, and runtime environments, Processing and analysis of data in computational physics/chemistry/biology, Distributed multimedia analysis and processing, Multi-core and cluster computing

Edge/Cloud-Computing,    Quanteninformationstheorie und Quanteninformatik

The International Science Council’s Committee on Data (CODATA) and World Data System (WDS), and the Research Data Alliance (RDA)

International Data Week brings together a global community of data scientists and data stewards; researchers from all domains; data, interoperability and informatics experts from all fields; industry leaders, entrepreneurs and policymakers.

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

What could be a bigger source of "big data" than the entire Universe? Researchers in statistics and computer science are developing new methods to analyse the "big data" that companies and governments want to understand. To test out these methods they need big datasets that everyone can share --- and new astronomical telescopes just happen to be gearing up to generate observations of billions of stars and galaxies that fit the bill. Insights from statistics will help astrophysicists to get the most out of these observations. The Astrostatistics 2023 workshop will bring together statisticians and astrophysicists to learn the technical languages of the two fields and identify how to work together to combine the excitement of space with big data.

Astronomie, Astrophysik und Kosmologie,    Statistik, Spieltheorie

Extreme computing plays a major role in our energy business. Looking across multiple industries, our business undoubtedly exploits the largest high-performance computing (HPC) capacity. Extreme computational simulations and data analytics enable high productivity by making better use of huge amounts of data. Although computational capability is accelerating, we have yet to see how extreme its effects are on pushing upstream to transform its business model. Therefore, algorithms, relating to big data, AI, IoT, HPC and cloud computing, performing and scaling as best as possible, on the latest computing platforms, will have a direct impact on many of the decisions reshaping our industry. The workshop will enable all participants to get acquainted with the very latest advances in HPC architectures and how these may be applied in geoscience applications used in the oil and gas industry and energy transition.

Petrochemie und Ölindustrie,    Hochleistungsrechner, Parallelrechner

The Minerals, Metals & Materials Society (TMS)

The 3rd World Congress on High Entropy Alloys (HEA 2023) is a cross-disciplinary technical forum designed to share the latest research advances in single-phase and multiphase metallic, intermetallic, and ceramic high entropy materials for functional or structural applications. HEA 2023 will feature highly focused technical talks on topics that include, but are not limited to, fundamental theory of alloy design, computational modeling and simulation, properties, processing, and applications of high entropy alloys.

Metallurgie und Werkstoffkunde,    Angewandte Physik (allgemein)

CIRM – Centre International de Rencontres Mathématiques

Simulation,    Computerphysik und Numerische Simulation

Differential equations are at the forefront of modeling real-world systems and applications arising from different fields of science and engineering. This SEAMS school brings together experts in numerical analysis and scientific computing to give introductory lectures on recent numerical techniques in solving ordinary and partial differential equations, and their applications. These lectures are intended for graduate students and early-career mathematicians from the Philippines and neighboring countries. The school will be composed of lecture series and hands-on computational exercises.

Kurse und Veranstaltungen für Studenten der Mathematik,    Infinitesimalrechnung, Differentialgleichungen, Integralrechnung

California Institute of Technology (Caltech)

This annual workshop welcomes academics and industry practitioners at the interface of theoretical physics, mathematics, and computer science to discuss research directions centred on the application of machine learning and data science to open problems in fundamental theory, and equivalently the application of techniques from theory to describe the successes of machine learning.

Institute of Mathematical Statistics (IMS)

The objective of ICSDS is to bring together researchers in statistics and data science from academia, industry, and government in a stimulating setting to exchange ideas on the developments of modern statistics, machine learning, and broadly defined theory, methods, and applications in data science.

CIRM – Centre International de Rencontres Mathématiques

Informations & Wissensmanagement, Big Data Computing,    Netzgemeinschaften und Gesellschaftliche Aspekte der Informationstechnologie

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Many big data sets in various application domains have complex relationships, which can be modelled as graphs, consisting of entities and relationships between them. Consequently, graphs are extensively studied in both Mathematics and Computer science. In particular, planar graphs, which can be drawn without edge crossings in the plane, form a distinguished role in Graph Theory and Graph Algorithms. Many structural properties of planar graphs are investigated, in terms of excluded minors, low density, and small separators, which lead to efficient algorithms for planar graphs. Consequently, fundamental algorithms for planar graphs have been discovered. However, most real-world graphs, such as social networks and biological networks, are nonplanar. For example, the scale-free networks, which are used to model web graphs, social networks and biological networks, are globally sparse nonplanar graphs, with locally dense clusters and low diameters. To understand such real-world networks, we need to solve fundamental mathematical and algorithmic research questions on beyond-planar graphs, which generalize the notion of planar graphs, in terms of topological constraints or forbidden edge crossing patterns.

Graphentheorie und Kombinatorik,    Informations & Wissensmanagement, Big Data Computing

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The global fight against the COVID-19 pandemic has presented massive challenges and real opportunities for data science and visualization research and technology. Epidemiologists, virologists, infection control experts and public health officials are required to make sense of information from a huge variety of data sources. Physicians and medical researchers have access to rapidly changing medical data as the pandemic evolves. Political stakeholders (supported by economists, public health officials, and other experts analyzing the data) make decisions on which action to take, balancing the impact on public health with social and economic impacts. The general public is regularly presented with visualization, through news, conventional and social media, and government publications, and these inform their actions for the next days and weeks.

The Alan Turing Institute

Hosted by The Alan Turing Institute, AI UK 2024 will be an in-depth exploration of how data science and AI can be used to solve real-world challenges. Our diverse programme has been thematically structured around the latest innovations from across the AI ecosystem. With a broad range of interactive content, expect to hear the latest thinking on foundational AI, digital twins, algorithmic bias, AI ethics – and much more.

Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen,    Geschichte und Philosophie von Wissenschaft und Technik

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

There is currently an insufficient transfer of state-of-the-art research knowledge from computer science to research software engineering, and vice versa, in part leading to an incomplete understanding in computer science of the domain-specific and general challenges in research software engineering. This interactive seminar therefore brings the computer science and software engineering research community and the research software engineering community together to define a common language, and apply it to improve reciprocal knowledge transfer.

Softwareentwicklung,    Computerphysik und Numerische Simulation

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Metabolomic data, usually from mass spectrometry or nuclear magnetic resonance spectroscopy, is highly complex and information rich. With continuing advances in metabolite detection technologies, the volume and complexity of data is ever increasing. Computational analysis of metabolomic data, from raw data processing to biological interpretation, is therefore fundamental to realizing the impact of metabolomics on a diverse array of application fields, e.g., environmental toxicity, industrial biotechnology, and biomedicine. This Dagstuhl Seminar extends the Computational Metabolomics series to examine how we can enhance the utility and interpretation of metabolomics data.

Computerprogramme,    Algorithmen und Datenstrukturen

Institute for Mathematical and Statistical Innovation

Materials informatics is an emerging field defined by the use of simulation tools combined with methods from data sciences and machine learning to better understand materials properties and design innovative materials. The models which are considered cover an extremely wide range, from Schrödinger’s equation, which describes matter at the (sub)atomistic scale, to the equations of continuum mechanics. Mathematical sciences play a key role in materials informatics, both to construct the databases used to train machine learning algorithms (since these databases are made of reference simulation results), and to harness them in order to extract the most relevant information. The aim of this program is bring together a diverse scientific audience, both between scientific fields (physical sciences, materials sciences, biophysics, etc) and within mathematics (mathematical modeling, numerical analysis, statistics and data analysis, etc), to make progress on key questions of materials informatics.

Festkörperphysik,    Metallurgie und Werkstoffkunde

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

Kurse und Veranstaltungen für Studenten der Mathematik,    Komplexe Netzwerke

ICMS - International Centre for Mathematical Sciences

This workshop will gather researchers from the intersecting fields of inverse problems, uncertainty quantification, data analytics, machine learning, and related areas to discuss novel theory and new methods for big data challenges. A focused multifaceted research group gathered at one location is an excellent opportunity for established and early career researchers. The aim of this workshop is to foster a diverse, open, and friendly environment to advance this field, open new directions, and to stimulate new collaboration.

Informations & Wissensmanagement, Big Data Computing,    Angewandte Mathematik (allgem.)

The International Centre for Mathematical Sciences (ICMS)

This workshop will gather researchers from the intersecting fields of inverse problems, uncertainty quantification, data analytics, machine learning, and related areas to discuss novel theory and new methods for big data challenges. A focused multifaceted research group gathered at one location is an excellent opportunity for established and early career researchers. The aim of this workshop is to foster a diverse, open, and friendly environment to advance this field, open new directions, and to stimulate new collaboration. 

Organized by AIDIC, The Italian Association of Chemical Engineering

Society for Industrial and Applied Mathematics (SIAM)

This conference is co-located with the SIAM Conference on Mathematics of Planet Earth (MPE24). This is the conference of the SIAM Activity Group on Life Sciences. The life sciences have become increasingly quantitative as new technologies facilitate collection and analysis of vast amounts of data ranging from complete genomic sequences of organisms to satellite imagery of forest landscapes on continental scales. As a consequence, mathematics and computational science have become crucial technologies for the study of complex models of biological processes.

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

Kurse und Veranstaltungen für Studenten der Mathematik,    Geschichte und Philosophie von Wissenschaft und Technik

Casa Matemática Oaxaca (CMO)

Casa Matemática Oaxaca (CMO)

Our society faces various grand challenges that it urgently needs to address over the coming years, such as mitigating climate change, combating environmental damage, and providing effective, affordable healthcare for everyone. Data science and Artificial Intelligence are expected to play a key role in successfully tackling many of these challenges. While recent progress in AI was driven mostly by empirical developments, we are entering now a new phase in which questions of trustworthiness, scalability, fairness, privacy, reproducibility, and explainability emerge as key factors. Fundamentally new ideas and approaches are needed to address these open problems, which will require a close collaboration between mathematicians, statisticians, computer scientists, and engineers. This workshop features an interdisciplinary group of exceptional scientists from across the world to present recent developments, foster new interactions, and create novel ideas for the solution of the aforementioned data science problems.

Mustererkennung und Bildverarbeitung, Maschinelles Sehen,    Infinitesimalrechnung, Differentialgleichungen, Integralrechnung

CEA, Alternative Energies and Atomic Energy Commission

In addition to promoting the advances in the traditional topics of the conference, encompassing super-computing for nuclear applications and Monte Carlo simulation, SNA + MC2024 will also emphasize novel subjects with strong scientific, technological and societal stakes: the recent breakthroughs in High Performance Computing, environmental issues and energy transition.

Kernenergie,    Hochleistungsrechner, Parallelrechner

Society for Industrial and Applied Mathematics (SIAM)

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

Kurse und Veranstaltungen für Studenten der Mathematik,    Infinitesimalrechnung, Differentialgleichungen, Integralrechnung

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The vision of NFDI4DS is to support all steps of the complex and interdisciplinary research data lifecycle in Data Science and Artificial Intelligence. The overarching objective of NFDI4DS is the development, establishment, and sustainment of a national research data infrastructure for the Data Science and Artificial Intelligence community. The key idea is to work towards increasing the transparency, reproducibility and fairness of Data Science and Artificial Intelligence projects, by making all digital artifacts available, interlinking them, and offering innovative tools and services. Within this Dagstuhl Research Meeting, the NFDI4DS partners will reflect on their progress so far, and will work on their long-term strategy.

Informations & Wissensmanagement, Big Data Computing,    Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen

Simons Laufer Mathematical Sciences Institute (SLMath)

In a growing number of applications, one needs to analyze and interpret data coming from massive networks. The statistical problems arising from such applications lead to important mathematical challenges: building novel probabilistic models, understanding the possibilities and limitations for statistical detection and inference, designing efficient algorithms, and understanding the inherent limitations of fast algorithms. The workshop will bring together leading researchers in combinatorial statistics, machine learning, and random graphs in the hope of cross-fertilization of ideas.

combinatorial statistics, random graphs, network inference, network reconstruction, detection, estimation

Komplexe Netzwerke,    Graphentheorie und Kombinatorik