Topic: optimal transport

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.

Jessica Howard presents optimal transport as a common language for neural-network ensembles, statistical field theories and exact renormalisation-group flows. Viewing a field theory as a probability distribution over functions makes transport methods useful for numerical nonperturbative calculations and analytical insights. The talk connects these ideas to particle-physics data analysis and to feature learning and training dynamics in asymptotically wide networks.

Optimal transport is a mathematical method to define a distance between probability distributions. This is particularly useful in various domains, including physics, biology, machine learning, and economics, among others. After introducing the Optimal Transport (OT) problem at finite temperature, we show how it can be formulated as a statistical physics problem. This approach allows us to derive very efficient algorithms to effectively compute the distance between two probability distributions. The a priori unrelated Schrödinger bridge (SB) problem is presented, and it is shown to be a dynamic