Topic: model reduction

The Society for Industrial and Applied Mathematics convenes its triennial conference on applied and numerical linear algebra. The programme covers matrix computations, machine learning, quantum simulation, inverse problems, high-performance computing, dynamical systems, model reduction, optimal transport, network science, tensor methods and related applications. Minitutorial and minisymposium proposals are due 26 October 2026 and contributed presentation abstracts due 23 November 2026.

The Chair in Nonlinear Dynamics recruits a doctoral researcher to develop interpretable, data-driven models for complex physical systems. Work centres on spectral submanifold methods and nonlinear model reduction, with possible applications to fluid–structure interaction, materials with memory, soft robotics, teleoperation, space structures and turbulence. The doctorate can be based in mechanical engineering or mathematics according to the candidate’s background. The start is flexible, with an early start preferred. Apply online with a CV, bachelor’s and master’s transcripts and at least two r

Yuji Nakatsukasa explains how CUR decompositions approximate a matrix using selected columns and rows, without inspecting every entry once the indices are chosen. Near-optimal CUR approximations exist relative to the truncated singular value decomposition, and efficient algorithms make them useful for large problems. The talk covers computation and theoretical guarantees before exploring applications to approximation theory, model reduction, and parameter-dependent problems.