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Access to EasyChair and create your account to submit your abstract. All questions about submissions should be emailed to PDEs New CFP. My CFPs. Various techniques have been developed to reduce the computation to a boundary domain, including artificial boundary conditions, boundary integral equations, and perfectly matched layers. This mini-symposium aims at bringing both leading experts and young researchers working on these problems, to discuss the latest results and to exchange new ideas, approaches, possible applications and emerging computational techniques.

Abstract: Model-driven optimization and control is at the core of a myriad of modern scientific and societal challenges including the control and estimation of collective behaviour phenomena, the design of protocols for autonomous vehicles, and the study of data transmission over communication networks, to name a few. Furthermore, in an applicative framework a fundamental step to study real problems is represented by the introduction of stochastic parameters reflecting the natural lack of information due to a wide range of phenomena spanning form possible external actions to behavioral forces.

Therefore, the quantification of the influence of uncertainties is of paramount importance to build more realistic models and to design efficient automation strategies. This minisymposium addresses the analysis of novel computational techniques for robust control synthesis in the presence of parametric and structural uncertainties.

It aims at promoting interactions between researchers working on computational methods for Hamilton-Jacobi and Fokker-Planck type equations, multiscale interacting particle systems and kinetic equations, PDE-constrained optimization, and applications in collective dynamics.

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Abstract: We discuss recent advances in the numerical approximation of problems in fluid mechanics. The emphasis will be on finite element methods for Stokes and Navier-Stokes equations, but contributions in related areas such as discontinuous Galerkin, virtual elements or finite volume methods will also be welcome. In addition, contributions related to adaptive methods for one or several of the above topics will also be welcome. Abstract: There is a strong relationship between network science and linear algebra, as complex networks can be represented and manipulated using matrices.

Some popular tasks in network science, such as ranking nodes, identifying hidden structures, or classifying and labelling components in networks, can be tackled by exploiting the matrix representation of the data. In this minisymposium we sample some recent contributions that build on an algebraic representation of standard and higher-order networks to design models and algorithms to address a diverse range of network problems, including but not limited to core-periphery detection and centrality.

Abstract: Optimisation problems over continuous variables are ubiquitous across quantitative disciplines, and the development of efficient numerical methods to solve such problems is an important topic in numerical analysis.

This minisymposium will present recent work on a diverse array of continuous optimisation problems, including linear and nonlinear programming, and global and stochastic optimisation. Abstract: The theoretical and algorithmic aspects of rational approximation have experienced a surge of interest in recent years, motivated in part by applications of rational approximation in numerical linear algebra.


This minisymposium aims to bring together researchers working on these diverse aspects of rational approximation. Abstract: The mini-symposium focuses of the fundamental challenges of understanding uncertainties in numerical analysis. Addressing these challenges requires synergies across mathematical disciplines including statistical sciences as well as traditional numerical analysis.

The mini-symposium aims at presenting possible approaches to form this synergy by discussing, motivating, and highlighting approaches enabling the coherent propagation of probability measures through the numerical computation and inference. Recognizing a potential possibility of statistical view on the data employed in numerical schemes gives rise to a new understanding of the notion of dimensionality and important properties it entails, in particular in the case when dimension of the vector space to which the original data belongs is high.

Revealed intra-disciplinary connections between traditional and more recent approaches in numerical analysis enables linking of vast relevant accumulated expertise in numerical analysis to the vibrant community of Machine Learning and Artificial Intelligence. Abstract: Real life problems require expensive approximations and when such approximations are combined with other numerical methods the cost can become prohibitive.

Towards Data Science

This is especially relevant in stochastic problems where samples of an approximation must be computed to explore the probability space. Hierarchical methods, lead by Multilevel Monte Carlo, have been successfully applied in many problems of this kind with excellent results. By using a combination of solutions with low to high accuracy, the underlying numerical methods, such as Monte Carlo, are accelerated by orders of magnitude. In this mini-symposium, we discuss the recent advances and analysis of these hierarchical methods and their application to new problems.

Delegates may be interested in a related meeting taking place in Edinburgh on July. Systems theory, Control and Automation. The idea of the meeting is to get together researchers from two different areas of mathematics, namely Particle Systems and Partial Differential Equations, and to present recent scientific results in both areas. The primary goal of this conference is to bring together scientists and mathematicians working in partial differential equations and related fields. Contemporary challenges raised by recent advances in engineering, industry, and bio-technology, will be confronted with state-of-the-art mathematical and computational tools in PDE.

Some of the topics covered in this meeting can be found in the list of themes below. Advanced graduate students and young researchers are encouraged to participate.

Preconditioning and iterative methods for differential equations

Limited funding is available for graduate students and recent PhDs. Analysis of inverse problems through partial differential equations and related topics. Workshop — An approach of ordinary differential equation methods for nonlinear problems. Applications that involve HJ PDEs in a high-dimensional and possibly infinite-dimensional setting lead to challenging computational problems.

Although a large literature is available on HJ PDEs, many challenges remain both from a mathematical and computational point of view. The program will open with one week of tutorials, presented by some of the main organizers. These tutorials will provide an introduction to the major themes of the entire program and connect the themes of the four workshops. The goal is to introduce a common language and build a foundation for the participants of this program who have diverse scientific backgrounds.

During the weeks between workshops, participants in residence will develop collaborations; we expect fruitful discussions especially between domain scientists, algorithm developers and pure and applied mathematicians. The SouthEastern Analysis Meeting has been established as one of the principal annual events bringing together seasoned scholars and junior researchers to discuss various areas of analysis, including functional analysis, operator theory, complex analysis, harmonic analysis, and their applications.

Held in Dundee/Scotland, June 23–27, 1969

Event listing ID:. Numerical Analysis and Computational Mathematics. Ideas to be explored in the workshop include different computational methodologies for efficient real-time solution of nonlinear HJ equations in reachability, optimal control, and differential games. Ideas such as efficient reduced-complexity computation of optimal control solutions, exploiting structure to decompose the solution space to scalable computations and reduced-complexity feedback structures for efficient implementation of optimal controllers based on available data will be considered.

The Dundee Numerical Analysis Conferences

Periods, Motives and Differential equations: between Arithmetic and Geometry. This workshop will bring together researchers with background in PDEs, Inverse Problems, and Scientific Computing who are already working in machine learning, along with researchers who are interested in these approaches. We propose to organize a week-long workshop on Mean Field Games MFG and Applications for which we have a number of goals: i to gather together leading researchers in MFG and certain cognate areas economics, optimal transportation, finite dimensional Hamilton-Jacobi equations, Stochastic equations, applied control, numerics.

This week-long workshop on Mean Field Games MFG and Applications for has a number of goals: i to gather together leading researchers in MFG and certain cognate areas economics, optimal transportation, finite dimensional Hamilton-Jacobi equations, Stochastic equations, applied control, numerics. In spite of its flexibility, computing nonlinear problems in random media is very expensive.