Conférences - Physique numérique - États-Unis

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Machine Learning for Climate
01 nov 2021 - 04 nov 2021 • Santa Barbara, États-Unis
UC Santa Barbara, Kavli Institute for Theoretical Physics (KITP)
The theoretical understanding of the Earth system has fundamentally advanced in recent decades in parallel to an exponential increase of observations and modeling data. However, climate scientists cannot meet the challenge of informing society about changes that may occur in the future at regional and local scales because many two-way, multi-scale processes that encompass the physical chemical and biological realms continue to elude us. Big data and the associated algorithms (Machine Learning) provide the opportunity to learn about quantities related to the climate systems in ways and with an amount of detail that were infeasible only a few years ago. The opportunity for descriptive inference creates the chance for climate scientists to ask causal questions and create new theories or validate old ones. Furthermore, when paired with modeling experiments or robust research in model parameterizations, “big data” can provide data-driven answers to vexing questions.

This conference will set the stage for exchanging tools and ideas and will help identify key problems where consistent progress is achievable through collaborative efforts. The theme of the conference will extend more broadly than the Physics focus of the main program, in order to elicit input from a wide range of experts across the earth system and computational sciences who are involved in the climate change problem. Given the level of interdisciplinarity and exchange that we aim for and expect, this conference will summarize current understanding and open questions, and will set the stage for achieving the aims of the associated KITP program.

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Transport in Stellar Interiors
15 nov 2021 - 18 nov 2021 • Santa Barbara, États-Unis
UC Santa Barbara, Kavli Institute for Theoretical Physics (KITP)
Many of the largest uncertainties in the physics of stars are related to transport in their interiors, including the transport of heat, chemical elements, and angular momentum. Our current understanding of these processes has been challenged and refined by recent advances in observational data, including TESS and spectroscopic studies, by new work on 1D models, and by the ability to generate more sophisticated 3D simulations. This conference will bring together members of the community working on transport processes from a variety of angles, including observations, theoretical calculations, and stellar models, with an emphasis on how these techniques can be combined to improve our understanding of stellar physics.
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Gordon Research Conference — Computational Materials Science and Engineering
31 jul 2022 - 05 aou 2022 • Newry, États-Unis
Analytic and Geometric Aspects of Gauge Theory (GT)
22 aou 2022 - 21 dec 2022 • Mathematical Sciences Research Institute, Berkeley, États-Unis
The mathematics and physics around gauge theory have, since their first interaction in the mid 1970's, prompted tremendous developments in both mathematics and physics. Deep and fundamental tools in partial differential equations have been developed to provide rigorous foundations for the mathematical study of gauge theories. This led to ongoing revolutions in the understanding of manifolds of dimensions 3 and 4 and presaged the development of symplectic topology. Ideas from quantum field theory have provided deep insights into new directions and conjectures on the structure of gauge theories and suggested many potential applications. The focus of this program will be those parts of gauge theory which hold promise for new applications to geometry and topology and require development of new analytic tools for their study.
Identifiant de l'évènement:
New Mathematics for the Exascale: Applications to Materials Science
13 mar 2023 - 16 jui 2023 • Institute for Pure and Applied Mathematics (IPAM), Los Angeles, États-Unis
The aim of this program is to foster the development of new mathematical tools and formalisms that will enable a new generation of ultra-scalable algorithms for a broad range of applications in computational materials science. Topics of interest will include strategies for scalable single-scale simulations, novel massively-parallel scale-bridging algorithms, and integration of extreme-scale computing into experimental and data science workflows. The program will bring together applied mathematicians, materials scientists, computer scientists, and method developers interested in unlocking the potential of upcoming exascale architectures through novel mathematical approaches.
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Dernière mise à jour: 28 Juillet 2021