Conferences  >  Physics  >  Computational Physics and Numerical Simulation  >  Canada

Programming will feature all aspects of solidification modelling including solidification process technologies (continuous and semi-continuous casting, shape casting, additive manufacturing, and welding amongst others), coupled multi-physics simulations, defect formation, fluid flow, micro and macro structure formation, numerical methods, as well as related experimentation especially in-situ observation of solidification.

IEEE Microwave Theory & Techology Society (MTT-S)

NEMO'2023 will bring together experts and practitioners of electromagnetic and multiphysics-based modeling, simulation and optimization for RF, microwave and terahertz applications. This conference is an ideal forum to share new ideas on techniques for electromagnetic and multiphysics modeling, propose efficient design algorithms and tools, and anticipate the modeling/analysis needs of future technologies and applications.

Electrostatics, Electromagnetic Waves,    Electronic Engineering

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.

Calculus, Differential Equations and Integration,    Algorithms and Data Structures

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.

Nuclear Energy,    Applied Mathematics (in general)

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.

Systems Biology and Computational Biology,    Biophysics, Medical Physics

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.

Mathematical Physics,    Algorithms and Data Structures