Konferenzen  >  Physik  >  Computerphysik und Numerische Simulation

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.

Mathematische Physik,    Kurse und Veranstaltungen für Studenten der Mathematik

Forschungszentrum Jülich

Correlated systems are characterized by strong electron-electron interactions, resulting in many-body effects eluding a static mean-field description. They thus proved largely intractable until the development of dynamical mean-field theory (DMFT). In this approach, mapping the lattice onto an auxiliary impurity problem gives a local dynamical self-energy, which captures the essence of electronic correlations. DMFT is exact in the limit of infinite dimensions, and often provides an excellent approximation for real crystal structures. This crucial insight represented a paradigm shift in the study of correlations. Solving ever more complex impurity problems nowadays allows the simulation of real materials, making contact with experiment via the calculation of spectra, dynamical response functions, and non-equilibrium properties. Even subtle non-local effects can be captured using various approaches, including clusters of impurities or diagrammatic expansions. Thus DMFT finds application not only in correlated bulk systems but also in heterostructures, and can even be employed to understand the properties of topological phases of strongly correlated electron systems.

This year's school will introduce the concept of dynamical mean fields and explore how it can be used to understand the physics of real materials. Lectures will range from Fermi liquids and the limit of infinite dimensions to the physics of quantum impurities and their relation to the properties of correlated lattice systems.

The goal of the school is to introduce advanced graduate students and up to this modern method for the realistic modeling of strongly correlated matter.

Event Organization Team;     Email: correl22@fz-juelich.de

correlated systems, dynamical mean field theory, Fermi liquid, Mott transition, machine leaning, quantum impurities, out of equilibrium phenomena, topological phases, non-local effects, heterostructures, superconductivity

Quantenmaterialien, Tieftemperaturphysik,    Kurse und Veranstaltungen für Physikstudenten

This Beilstein Nanotechnology Symposium is organized by Iseult Lynch, University of Birmingham, and Georgia Melagraki, Hellenic Military Academy.

Nanoinformatics (as an offshoot of chemoinformatics) refers to the combination of physical chemistry and materials theory with computer science’s in silico approaches to address key questions such as the prediction of materials functionality, fate in the environment, toxicity or therapeutic ability, recyclability and more. As the properties of nanomaterials themselves span several scales, from electronic, atomistic, mesoscopic to continuum, and are highly dynamic and context dependent (i.e., interact with and are transformed by their surroundings as well as impacting on their surroundings), they introduce new challenges for naming, describing and representing, and require the combination of physics-based and data-driven modelling approaches. This symposium will highlight current advances towards prediction of nanomaterials interactions with living systems and the environment. The symposium will cover the whole range of nanoinformatics, from construction of in silico nanomaterials for simulation of their interactions with biomolecules, membranes, cells and organisms and integration into complex product matrices, through development of nanomaterials descriptors and their use in prediction of nanomaterials exposure, mechanisms of biological activity, hazard and risk assessment, to integration of models for different end-points including sustainability, recyclability, insurance, and mechanistic considerations into IATA, meta-models or multi-criteria optimisation models to simultaneously balance materials functionality, safety and sustainability.

Department of Physics, Sardar Vallabhbhai National Institute of Technology (SVNIT) Surat

The conference aims to improve and continue communication among researchers in functional materials and applied physics-related fields. It is anticipated that this conference will provide an outstanding opportunity for participants to exchange ideas and promote discussions on recent advances in the field of materials & other areas of physical sciences. The theme of the conference will be the “state-of-the-art” performance of nano-materials for future technologies. The scientific deliberations at the conference will cover a wide range of topics in materials & other areas of physical sciences in the form of invited talks, contributory papers, and panel discussions. In addition, there will be talks by Young Scientist Award nominees.

Email: fmap2021@svnit.ac.in

The scope of the conference will cover the following topics. (a) Functional Materials (b) Nano Materials (c) Optoelectronics (d) Phase Transition, Quantum Fluids, and Solids (e) Photonic materials & Plasmonics (f) Nano-biophysics (g) Glasses, Ceramics, Polymers & Composites (h) Surface, Interface & Thin Films (i) Superconductivity, Magnetism & Spintronics (j) Thermoelectric Materials (k) Energy Materials (l) 2D Materials (m) Theoretical Sciences (n) Applied Plasma Physics (o) Theoretical Sciences (p) Applied Physics

Festkörperphysik,    Clusterphysik, Nanowerkstoffe, Graphen und Fullerene

The aim is to train undergraduate, post-graduate, early-career and other international junior scientists from the fields of water and the environment, land surface science, water resources, ecology, hydrometeorology, biogeochemistry and other related research areas concerned with the delivery of next-generation land-surface and hydrological predictions. You will learn the technical skills to run JULES and standalone Hydro-JULES tools in order to investigate hydrological processes under current and future scenarios, understand how to use the technical features built in Hydro-JULES for collaboration in scientific projects.

Hydrologie,    Simulation

McPhase Project

This workshop in Venice aims to bring together new Users of McPhase with experienced Scientists working in the field. The small number of participants allows to focus on individual research topics and discuss basic use and advanced simulation techniques.

Secretariat;     Email: service@mcphase.de

McPhase, Magnetism, Neutron Scattering, Standard Model of localised Magnetism, Magnetostriction, Crystal Fields, Atomic Magnetism, Magnetic Structures, Magnetic Interactions, Numerical Simulation

Magnetismus, Spintronik,    Angewandte Physik: Neutronenstreuung

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.

Institute for Pure and Applied Mathematics (IPAM), UCLA

Institute for Pure and Applied Mathematics (IPAM), UCLA

The vast majority of the computing power available to the materials science community is consumed by a relatively small number of workhorse methods, such as molecular dynamics and density functional theory. These methods have been adapted to run on parallel platforms for decades, but the focus has firmly been on weak-scaling, i.e., on scaling the problem size with the number of processors. While high-performance weak-scaling implementations of these methods are extremely valuable, the focus on increasing length-scales limits opportunities for scientific discovery. Transformative impact requires the capability to leverage computing resources to simultaneously and flexibly increase length scales, time scales, and accuracy. Increasing simulation timescales requires a deep understanding of the mathematics of rare in order to inform novel methods that are specially tailored from the start, as well as strong-scaling computational engines that can leverage large computational resources on problems of relatively small sizes. This requires a dramatic rethinking of how basic algorithms in materials science are derived and implemented. Similarly, the exponential increase in computer resources now enables very high accuracy simulations with methods such as coupled clusters or quantum Monte Carlo. These methods are extremely powerful, but they tend to scale poorly with the number of electrons. The development of new flexible methods where accuracy can be systematically adjusted in order to modify the tradeoff between size and time scales would therefore be extremely beneficial. This workshop will focus on recent development of new mathematical approaches to intensive calculations at massive scale with a focus on new ways to improve scalability (both weak and strong) and extend simulations along the size, time, and accuracy axes simultaneously.

Part of the Long Program New Mathematics for the Exascale: Applications to Materials Science, Integration of direct simulations, online data analysis, and experimental data. Mathematical methods for data assimilation. Large-scale inverse problems. Computation-aided online experimental design at massive scales. Active exploration of chemical space using massive quantum calculations. Workflow infrastructure. Integration of numerically-intensive calculations with ML/data-science at scale.

Wissenschaftliches Rechnen und Datenwissenschaft,    Festkörperphysik

Okinawa Institute of Science and Technology (OIST)

The goal of the Conference is for the participants to discuss the latest research results in data-driven science (such as artificial intelligence, machine learning, image processing, data mining, etc.) applied to plasma science and technologies, including, but not limited to, plasma processing and nuclear fusion. Thanks to the recent advancement of sophisticated diagnostics techniques and first-principle computer simulations, the amount of data that one accumulates in research is increasing exponentially. Plasma scientists should take full advantage of, and may even contribute to, the latest development of data-driven science and find a way to extract essential information efficiently from such data.

Institute for Pure and Applied Mathematics, UCLA.

Quantitatively predicting the properties of “real” structural materials is an extremely challenging endeavor, as macroscale properties depend on characteristics of the material at every scale, from nanometers (short range order, point defects, etc.), to micrometers (grain size, extended defects, etc.), to centimeters (texture, etc.). Similarly, relevant timescales range from atomic vibrations (picoseconds) to microstructural evolution times (hours to years). This extreme breadth of size and time scales makes the accurate simulations with fully-resolved atomic-scale tools (e.g., molecular dynamics) hopeless. Practical solutions must therefore rely on scale-bridging approaches that systematically upscale the lower scale physics into computationally tractable higher-scale constructs. The premise of this workshop is that extreme-scale computing can breathe new life into the field of multiscale modeling by addressing the problems identified above with brute force computing. This workshop will focus on new mathematical approaches to multiscale/multiphysics modeling, with a particular emphasis on the many theoretical and numerical challenges faced at the exascale. The goal is to bring together specialists in a range of massively parallel algorithms and researchers interested in improving the scalability of current techniques.

Part of the Long Program New Mathematics for the Exascale: Applications to Materials Science, Integration of direct simulations, online data analysis, and experimental data. Mathematical methods for data assimilation. Large-scale inverse problems. Computation-aided online experimental design at massive scales. Active exploration of chemical space using massive quantum calculations. Workflow infrastructure. Integration of numerically-intensive calculations with ML/data-science at scale.

Wissenschaftliches Rechnen und Datenwissenschaft,    Festkörperphysik

ICMS - International Centre for Mathematical Sciences

Waves on the surface of a fluid, or on the interface between different fluids are ubiquitous phenomena with wide importance in technology, science and the environment. Over the past fifty years the advent of numerical computations have meant that fundamental questions about waves have proven to be fertile ground for numerical approaches, and additionally rigorous theory, numerical and asymptotic methods have intersected to furnish a vibrant interdisciplinary ground for fundamental research.

This workshop will bring together mathematicians and other scientists working on numerical, asymptotic and rigorous aspects on a variety of free surface flows.

Angewandte Mathematik (allgem.),    Thermodynamik, Hydrodynamik und Statistische Mechanik

Institute for Pure and Applied Mathematics (IPAM), UCLA

The final workshop of this program will be an event designed to promote the generation of tangible products from the long program and to explore the best practices in setting up a computational co-design process that ranges from derivation of the key formalisms, to the design of the computational approach, and finally to its implementation. The vision of the program is that the long-term participants will self-organize into working groups designed to identify, analyze, and solve key problems that impede the effective use of extremescale computing in materials science. In order to ensure that these advances make their way into the hands of the community at large, this IPAM “hackathon” will gather code developers, mathematicians, method developers, and computer scientists and engineers from the computer vendors for a week of discussion, hands-on development, and implementation of the new ideas generated during the program. A key objective will be to ensure the transition from “research” codes into “production” codes that can be used and further improved by the community. As such, significant parts of the workshop will involve participants organizing into working groups in order to solve (or take the first steps in solving) practical issues that will have been raised throughout the program. The workshop will include a short series of talks where lead developers of various projects will share their experience and processes when attacking new problems related to evolving architectures, either in terms of new mathematical formalism, increasing computational scales, and architectural details.

Part of the Long Program New Mathematics for the Exascale: Applications to Materials Science, Integration of direct simulations, online data analysis, and experimental data. Mathematical methods for data assimilation. Large-scale inverse problems. Computation-aided online experimental design at massive scales. Active exploration of chemical space using massive quantum calculations. Workflow infrastructure. Integration of numerically-intensive calculations with ML/data-science at scale.

European Geosciences Union (EGU)

With the birth of geosciences and then of computational geosciences, people's attitude towards the world has changed. People are no longer passive observers but rather more proactive players in their environment and the knowledge gained from computational geosciences has been used to save lives and prepare for future scenarios. Now, with the Exascale computing era dawning upon us, even more accurate, clearer and faster data will be within our reach.

Geophysik und Geologie,    Wissenschaftliches Rechnen und Datenwissenschaft

MIAPbP - Munich Institute for Astro-, Particle and BioPhysics

Domains such as cosmology, astrophysics, nuclear or particle physics rely heavily on the use of stochastic simulation to design data analyses and perform inference with respect to fundamental physics theories. With the advances of Deep Learning, Bayesian inference techniques, a new frontier has come into view that focuses on the extension of traditional scientific programming along the two distinct directions of differentiable programming and probabilistic programming. The former plays an important role in navigating high-dimensional spaces, while the latter can manifestly incorporate generative modeling and inference into scientific data analyses. Both promise to expand the science reach of data-intensive domains through, e.g., the integration of domain knowledge and symmetries into neural networks, algorithmic robustness, alignment of science and training optimization goals, uncertainty quantification, causal inference, and more complete inference. However, most production scientific pipelines are not yet compatible with these techniques and their widespread adoption would represent a deep change in the scientific software landscape. This program will provide the opportunity for researchers from Computer Science and Fundamental Physics to develop applications as well as strategies for integrating these paradigms into existing scientific workflows and to close the gap of what is theoretically possible and practically deployed.

Teilchenphysik und Felder,    Wissenschaftliches Rechnen und Datenwissenschaft

MIAPbP - Munich Institute for Astro-, Particle and BioPhysics

We are happy to announce that the Differentiable and Probabilistic Programming for Fundamental Physics program includes a three day workshop. The workshop will contain talks about recent advancements in differentiable programming (DP) and probabilistic programming in the context of Physical Sciences. The workshop's diverse set of invited speakers will cover DP/ProbProg users and experts, toolmakers and authors of promising technologies. The workshop aims to explore the potential science impact of DP/ProbProg and to understand the steps towards, and challenges of, a successful implementation of these paradigms in fundamental physics.

Teilchenphysik und Felder,    Wissenschaftliches Rechnen und Datenwissenschaft

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

This workshop will bring together mathematicians with expertise spanning the theoretical, numerical, discrete and computational geometry and partial differential equations (PDEs) and computer scientists with related interests to identify and discuss some of the most promising areas for advances in the understanding and application in geometric data analysis.

Infinitesimalrechnung, Differentialgleichungen, Integralrechnung,    Algorithmen und Datenstrukturen

Mathematics and Computation Division of the American Nuclear Society

The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2023) is a part of a series of topical meetings organised by the Mathematics and Computation Division of the American Nuclear Society. The M&C conferences, held every two years, represent a series of international forums organised and sponsored to bring together worldwide expertise related to nuclear science or technology including mathematical and computational methods, numerical analysis, computer codes, computer architectures, and benchmarks for computationally solving problems in all disciplines encompassed by the Society.

Kernenergie,    Angewandte Mathematik (allgem.)

American Nuclear Society (ANS)

Kernenergie,    Thermodynamik, Hydrodynamik und Statistische Mechanik

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

Simulation,    Mathematische Physik

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

The interface between biology and quantitative disciplines including physics, mathematics and engineering is rapidly expanding. This is the result of many factors, but is largely driven by higher resolution spatial and temporal data from cutting edge imaging techniques, and increasing success in applying physical modeling techniques to biological problems. Mechanobiology is an especially rapidly developing field at the quantitative biology interface. This field describes how force is generated in biological systems, how force impacts chemistry, and how force generation is controlled by intracellular signaling pathways. Numerous feedforward and feedback interactions connect forces in cells to the dynamics of proteins and gene expression, resulting in highly nonlinear systems with complex and often non-intuitive behaviours.

Systembiologie und mathematische Biologie,    Biophysik

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

Most of the many discrete optimization problems arising in the sciences, engineering, and mathematics are NP-hard, that is, there exist no efficient algorithms to solve them to optimality, assuming the conjecture that P does not equal NP. The area of approximation algorithms focuses on the design and analysis of efficient algorithms that find solutions that are within a guaranteed factor of the optimal one. Loosely speaking, in the context of studying algorithmic problems, an approximation guarantee captures the quality of an algorithm -- for every possible set of input data for the problem, the algorithm finds a solution whose cost is within this factor of the optimal cost. A hardness threshold indicates the difficulty of the algorithmic problem -- no efficient algorithm can achieve an approximation guarantee better than the hardness threshold assuming that P does not equal NP. Over the last two decades, there have been major advances on the design and analysis of approximation algorithms, and on the complementary topic of the hardness of approximation. The goal of the workshop is to focus on a few key topics that could lead to deep new results in the areas of approximation algorithms, combinatorial optimization, hardness of approximation, and proof complexity.

Mathematische Physik,    Algorithmen und Datenstrukturen

CIRM – Centre International de Rencontres Mathématiques

Simulation,    Wissenschaftliches Rechnen und Datenwissenschaft