GAMM2025

Session

MS01: Hybrid algorithms by combining machine learning with multilevel and domain decomposition methods

MS01

8 Apr 2025, 14:00

Lecture and Conference Centre

Conveners

MS01: Hybrid algorithms by combining machine learning with multilevel and domain decomposition methods: MS01.01

• Janine Weber
• Alena Kopanicakova

696. Divide, Learn, and Conquer in Image Classification

Axel Klawonn

08/04/2025, 14:00

In many modern computer application problems, the classification of image data plays an important role. Among many different supervised machine learning models, convolutional neural networks (CNNs) and linear discriminantan alysis (LDA) as well as sophisticated variants thereof are popular techniques. Divide-and-conquer algorithms in combination with machine learning methods have been proven...

697. Additively Preconditioned Trust Region Strategies for Machine Learning

Rolf Krause

08/04/2025, 14:30

We present and discuss a parallel training strategy for the training of neural networks. Our strategy is based on a domain decomposition-like approach, which is combined with trust region as a convergence control strategy. The resulting additive non-linear preconditioner APTS (Additively Preconditioned Trust-region Strategy) provides a general framework for the parallel training of neural...

698. Domain Decomposition for Randomized Neural Networks

Alexander Heinlein

08/04/2025, 15:00

Neural network architectures based on overlapping domain decomposition approaches have emerged as a powerful framework for enhancing the efficiency, scalability, and robustness of physics-informed neural networks (PINNs). In this work, we apply this approach to randomized neural networks (RaNNs) for solving partial differential equations (PDEs). A separate neural network is independently...

699. Multilevel and parallel approaches to enhance the training of Transformers

Marc Salvadó Benasco

08/04/2025, 15:30

Over the last years, Transformer-based models have achieved cutting-edge results in areas such as natural language processing, computer vision, multimodality, and robotics due to the parallelization of their attention mechanism and its direct access to distant tokens in the sentence. Nonetheless, such parallelization can only be carried out along the sentence length, not the number of layers...