Conferences  >  Mathematics  >  Numerical Analysis and Computational Mathematics

Analysis,    Calculus, Differential Equations and Integration

Institute for Computational and Experimental Research in Mathematics, Brown University, Providence, RI (ICERM)

A universal source of difficulty in the numerical solution of differential equations is stiffness, which may stem from multi-scale dynamics and means that straightforward explicit discretizations are very inefficient. Consequently, choosing how to deal with stiffness (usually through the selection of the time discretization method) is often the most impactful decision made while solving these problems; it can be the difference between complete intractability and rapid solution.

Society for Industrial and Applied Mathematics (siam)

Society for Industrial and Applied Mathematics (SIAM)

Applied Mathematics (in general),    Algorithms and Data Structures

The subject of nonlinear analysis is of interest in its own right, and it also serves to lay the foundations for different fields of pure and applied mathematics. Researchers across the world are actively involved in analysing and developing different theories of mathematics which are applicable to real-world problems. Through this conference, the recent progress and advances in the different fields of mathematical analysis will be shared. We try to bring fruitful research directions and collaborations in this field.

Analysis,    Calculus, Differential Equations and Integration

Ghent Analysis & PDE Center, Ghent University

The purpose of the conference is to provide an international forum to present and discuss the latest developments in computational and applied mathematics in various emerging engineering and mathematical fields.

Email: acomen2025@ugent.be

Applied and computational mathematics, Computational solid mechanics, Computational fluid mechanics, Computational material science, Computational electromagnetism, Computational natural sciences and biomechanics, Data driven and Artificial Intelligence applied to modelling, Fluid-structure interactions and coupled problems, High performance scientific computing, Inverse problems, Machine learning, New and emerging discretisation methods, Numerical analysis, Optimisation methods, Partial Differential Equations, Software for scientific computations, Simulation of manufacturing processes, Uncertainty quantification and stochastic modelling

Applied Mathematics (in general),    Modeling and Simulation

The Romanian Society of Applied and Industrial Mathematics

CAIM series provide a forum for the review of the recent trends in applied and industrial mathematics either from a qualitative or from a numerical point of view. The conference has also sections for computer sciences and the theoretical branches of mathematics like algebra or geometry. A special section of the conference is dedicated to the mathematical teaching.

Phone: [0744427589];     Email: conferences.caim@yahoo.com

Partial Differential Equations; Ordinary Differential Equations, Dynamical Systems; Mathematical Modeling in Engineering and Life Sciences; Real, Complex, Functional and Numerical Analysis; Probability Theory, Mathematical Statistics, Operations Research; Algebra, Logic, Geometry; Computer Science; Education

Applied Mathematics (in general),    Modeling and Simulation

The goal of the conference is to share knowledge and the latest research on the following topics: approximation by linear and nonlinear operators, numerical integration, orthogonal polynomials, integral equations, inverse problems and applications of integral models, and related issues in numerical linear algebra.

Open Data Science Conference

ODSC West will feature 250+ speakers, instructors, and practitioners, 2,500 of your fellow data and AI practitioners, and hands-on instruction for world-class experts in: AI Agents, GenAI, LLMs, AI Engineering, Machine Learning, Deep Learning, AgentOps, LLMOps, MLOps, Ai for Robotics, Data Engineering, RAG, Reasoning, AI Safety & Security & more. You’ll also be able to build meaningful connections with speakers and peers in a relaxed setting during our networking events like the ODSC Networking Reception, Dinner with Data Scientists, Meet the Speaker session, Book Signing sessions, and more.

Phone: [none];     Email: iryna@odsc.com

ODSCWest, AI, Data Science, LLMs, Machine Learning

Neural Networks and Artificial Intelligence, Machine Learning,    Optimization Theory, Operational Research

Institute for Computational and Experimental Research in Mathematics, Brown University, Providence, RI (ICERM)

This workshop encompasses three major aspects of computation within Representation Theory and Algebraic Combinatorics. One concerns the development of efficient algorithms to compute important quantities in order to understand and classify them better. Such problems include structure constants and representation theoretic multiplicities, mutation invariants in cluster algebras, computing dimensions of coinvariant rings, characters of finite-dimensional representations, coefficients of Kazhdan-Lusztig polynomials, web bases etc. This is closely related to understanding what optimality we could expect and in particular the computational complexity aspects of those problems. Their computational complexity class can also be used to understand the existence of combinatorial interpretations, in particular for major structure constants lacking positive formulas like Kronecker and plethysm coefficients. On the other hand, representation theory has seen important applications within computational complexity theory, in the context of Geometric Complexity Theory and Quantum Information Theory.

University of Batna 2, Faculty of Mathematics and Computer Science

Control and Stabilization of PDEs, Operator Theory and Applications, Stochastic Analysis and Applications, Fractional Calculus and Its Applications, Integral and Integro-differential Equations, Differential Equations and their Applications, Numerical Methods, Algorithms and Approximation Theory, Mathematical Modeling in Engineering and Life Sciences, Probability Theory and Mathematical Statistics, Differential Geometry and its Applications, Dynamical Systems and its Application, Optimization and Operations Research, Fixed Point Theory and Applications, Inverse and imaging problems, Algebra and Number Theory, Cryptography and Security, Fuzzy Sets and Systems, Artificial Intelligence

Applied Mathematics (in general),    Calculus, Differential Equations and Integration

ICERM/Brown University

Statistical models are almost never right. All models involve certain parametric and structural assumptions. Bayesian nonparametric inference (BNP) is an increasingly widely used approach to mitigate the dependence on such assumptions. After a rapid expansion of BNP research over the past 30 years, BNP is now a mature research area in statistics and machine learning. The current main challenges are related to computational hurdles and bottlenecks and the closely related need to tackle more complex and highly structured problems. This program bring together researchers working in BNP, including computation, foundations, methodology and application of BNP methods, with the goal of identifying newly emerging computational strategies and inference approaches. The program and invited talks are planned to balance theoretical expertise, interest and prowess in computational methods, and exposure to selected substantial application areas. The intended nature of the program as identifying synergies of different approaches and potentially new research directions naturally leads to favoring breath over depth, with more emphasis on covering diverse areas rather than on in-depth discussions of a single specific theme.

Phone: [4018635030];     Email: info@icerm.brown.edu

Association of Nepalese Mathematicians in America, Nepal Mathematical Society, and Central Department of Mathematics at Tribhuvan University

The conference provides a platform for a diverse group of scientists applying mathematics to natural and health sciences, engineering, and finance. Specific areas covered include, but are not limited to, differential equations, computational mathematics, mathematical biology, statistics, data science and analytics, mathematics education, analysis, topology, geometry, algebra, number theory, optimization, operations research, quantitative finance, biomedical science, biophysics, and public health. The emphasis will be on bringing together mathematicians and scientists from all over the world for discussing and disseminating current research trends so as to provide opportunities for participants particularly from Southeast and East Asian countries for initiating research collaborations and building professional network.The conference features six plenary lectures delivered by renowned scientists and educators, special lectures presented by experts in diverse research topics, parallel sessions on invited and contributed talks, and poster presentations.

Email: khana66a@erau.edu

Differential Equations and Nonlinear Analysis, Mathematical Biology, Algebra, Geometry, Topology, Probability, Statistics, Data Science, Mathematics Education, Numerical Analysis, Scientific Computation, and Optimization

Applied Mathematics (in general),    Calculus, Differential Equations and Integration

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

The fields of kernel approximation and Gaussian processes have evolved, mostly independently, over the past few decades to become mature subjects in numerical analysis and statistics/applied probability, respectively. The two fields have natural affinities, both by relying on a common mathematical machinery of reproducing kernel Hilbert spaces and positive definite functions, and by treating a diverse assortment of scientific applications. A hallmark problem for kernels is the modeling of irregularly sampled, deterministic data, but kernels are also heavily employed in the simulation of a range of phenomena from atmospheric flows to financial forecasting.; a key feature of kernel approximation is the ability to deliver solutions to computational problems without requiring costly underlying geometric structures like meshing. Gaussian processes have traditionally focused on regression, classification, and learning of the underlying probability distribution from random samples, although recent activity has focused on successful undertakings in interpretable and explainable machine learning, inverse problems and partial differential equations with stochastic parameters. Despite their different languages and different objects of interest, the fields share a similar collection of important, challenging theoretical and computational problems.

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

The 5-day workshop, "Space-time Approaches to PDE Solvers: Unlocking the Power of Co-Design'', will bring together a diverse group of leading international experts in numerical analysis, scientific computing, and hardware-software integration to review and explore the cutting-edge developments and challenges in the simulation of complex PDEs. The workshop will focus on three major themes: i) space-time discretization methods for PDEs; ii) co-design principles for the next generation of PDE solvers; and iii) real-world applications for these advanced solvers, ranging from fluid dynamics and Earth system modeling to plasma-based energy systems to biomedical simulations and personalized medicine.

Applied Mathematics (in general),    Calculus, Differential Equations and Integration