AI in Scientific Computing – Training School

The training school develops three foundations for AI in scientific computing. Daniel Bartl introduces statistical learning through high-dimensional probability, explaining how the geometry and complexity of function classes govern generalization. Stanislav Budzinskiy covers floating-point errors, deterministic and stochastic rounding, matrix multiplication, mixed precision and conditioning in neural-network training and inference. Maximilian Herde introduces neural operators and foundation models for partial differential equations, studying pretraining, scaling and transfer to new physical problems, with practical Python exercises.

The school meets in person at RICAM, Science Park 2, fourth floor, Altenberger Straße 69, Linz, on 5–9 October. Monday through Thursday lectures start at 09:00 and run to 12:30, with discussion and exercise sessions generally scheduled 13:30–17:00; Wednesday has an excursion. Friday’s lectures run 09:00–11:30. Times are Europe/Vienna, CEST (UTC+02:00). The registration form still lists the Training School and requires an institutional email address. Registration is binding; participants should review the organizer’s cancellation and non-attendance policy. The event begins shortly after this listing’s publication.