Data-driven methods for partial differential equations

Session

Invited talks 4

Mar 3, 2026, 10:30 AM

Room 1.067 (Math Building (20.30), Karlsruhe Institute of Technology)

Conveners

Invited talks 4

• Sebastian Krumscheid

11. Finite-precision arithmetic in deep learning for scientific computing

Philipp Petersen (Universität Wien)

3/3/26, 10:30 AM

Invited talk

Deep learning methods are increasingly deployed using low-precision arithmetic, primarily driven by memory, energy, and throughput constraints. At the same time, deep neural networks are highly compositional systems, a structure that naturally raises concerns about the amplification and accumulation of numerical errors across layers and operations. Nonetheless, such models are being applied at...

19. Optimal Sensor Placement for Linear Inverse Problems via Measure Optimisation

Andrew Duncan (Imperial College London)

3/3/26, 11:15 AM

Invited talk

In PDE-based inverse problems, only a limited number of sensors can be deployed, so choosing measurement locations is crucial, but the resulting design problem is highly nonconvex. This talk explores how we can lift sensor placement from selecting B points to optimising over probability measures on the design domain, giving a tractable relaxation with a Bayesian interpretation. We then solve...