Tensor computation extends the principles of linear algebra and numerical analysis to higher-dimensional data structures, allowing for efficient processing of complex data. Tensors play a central role in a variety of disciplines, from machine learning to scientific simulations. Tensors are among the most versatile and powerful mathematical tools available, with applications in many disciplines such as physics, computer science, engineering, graphs, and machine learning.

The mini-course is designed as a comprehensive introduction to tensors and their applications, bridging theory and practice. It seeks to demystify the subject for beginners while offering valuable insights for those with more advanced knowledge. Our focus is twofold: 

Conceptual Understanding: By presenting tensors in a clear and accessible manner, we aim to equip the  course attendees with a solid grasp of the core principles. 

Practical Applications: With numerous examples and case studies, we plan to illustrate how tensors are used to address real-world challenges, from simulating physical systems to enabling breakthroughs in artificial intelligence.

Registration can be completed here.

Tensor computation extends the principles of linear algebra and numerical analysis to higher-dimensional data structures, allowing for efficient processing of complex data. Tensors play a central role in a variety of disciplines, from machine learning to scientific simulations. Tensors are among the most versatile and powerful mathematical tools available, with applications in many disciplines such as physics, computer science, engineering, graphs, and machine learning.

The mini-course is designed as a comprehensive introduction to tensors and their applications, bridging theory and practice. It seeks to demystify the subject for beginners while offering valuable insights for those with more advanced knowledge. Our focus is twofold: 

Conceptual Understanding: By presenting tensors in a clear and accessible manner, we aim to equip the  course attendees with a solid grasp of the core principles. 

Practical Applications: With numerous examples and case studies, we plan to illustrate how tensors are used to address real-world challenges, from simulating physical systems to enabling breakthroughs in artificial intelligence.

Registration can be completed here.

Seminarof Analysis

Speaker: Prof. Lahcen Laayouni (School of Science and Engineering, Al AkhawaynUniversity).

Date/time: 25/06/2025 (Wednesday), from 14:15 to 15:15.

Location:  Room 4.6, Building VIII.

Title:  Optimized Schwarz Methods: Challenges andPromises at Continuous and Algebraic Levels

Abstract:  Optimized Schwarz methods are a class ofdomain decomposition techniques designed to improve the convergence ofiterative solvers for partial differential equations (PDEs) by imposingoptimized transmission conditions. This talk explores the challenges and promisesof these methods at both continuous and algebraic levels. At the continuouslevel, we address the design of effective transmission conditions, theirdependence on problem-specific parameters. At the algebraic level, we examinethe construction of algebraic block transmission conditions for robustpreconditioners. Through theoretical and numerical results, we show thepotential of optimized Schwarz methods to handle large-scale PDE problems.