AA5 – Variational Problems in Data-Driven Applications


AA5-2 (was EF1-15)

Robust Multilevel Training of Artificial Neural Networks

Project Heads

Michael Hintermüller, Carsten Gräser (until 12/2022)

Project Members

Qi Wang

Project Duration

01.02.2023 − 31.01.2026

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Multilevel methods for training nonsmooth artificial neural networks will be developed, analyzed and implemented. Taylored refining and coarsening strategies for the optimization parameters in terms of number of neurons, layers and the network architecture will be studied. Efficient nonsmooth optimization methods will be introduced and used to treat the level-specific problems. The framework will be applied to problems that have a multilevel structure: learned regularization in image processing, neural network-based PDE solvers and learning-informed physics. Software will be developed and made publicly available.

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