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Eine Liste aller Seiten, die das Attribut „Kurzfassung“ mit dem Wert „Graph Neural Networks (GNNs) have shown great potential for use cases that can be described as graphs. However, training GNNs presents unique challenges due to the characteristics of graph data. The focus of this thesis is to examine their learning abilities by developing a GNN-based surrogate model for the injection molding process from materials science. While numerical simulations can model the mold filling accurately, they are computationally expensive and require significant trial-and-error for parameter optimization. We propose representing the mold geometry as a static graph and constructing additional node and edge features from domain knowledge. We plan to enhance our model with a self-attention mechanism, allowing dynamic weighting of a node's neighbors based on their current states. Further improvements may come from customizing the model’s message passing function and exploring node sampling methods to reduce computational complexity. We compare our approach to conventional machine learning models w.r.t. predictive performance, generalizability to arbitrary mold geometries and computational efficiency. This thesis is a follow-up work to a bachelor thesis written at the chair in 2022.“ haben. Weil nur wenige Ergebnisse gefunden wurden, werden auch ähnliche Werte aufgelistet.

Hier sind 2 Ergebnisse, beginnend mit Nummer 1.

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Liste der Ergebnisse

Efficient Training of Graph Neural Networks for Dynamic Phenomena (Proposal) + (Graph Neural Networks (GNNs) have shown gr … Graph Neural Networks (GNNs) have shown great potential for use cases that can be described as graphs. However, training GNNs presents unique challenges due to the characteristics of graph data. The focus of this thesis is to examine their learning abilities by developing a GNN-based surrogate model for the injection molding process from materials science. While numerical simulations can model the mold filling accurately, they are computationally expensive and require significant trial-and-error for parameter optimization. We propose representing the mold geometry as a static graph and constructing additional node and edge features from domain knowledge. We plan to enhance our model with a self-attention mechanism, allowing dynamic weighting of a node's neighbors based on their current states. Further improvements may come from customizing the model’s message passing function and exploring node sampling methods to reduce computational complexity. We compare our approach to conventional machine learning models w.r.t. predictive performance, generalizability to arbitrary mold geometries and computational efficiency.</br></br>This thesis is a follow-up work to a bachelor thesis written at the chair in 2022.helor thesis written at the chair in 2022.)

Efficient Training of Graph Neural Networks for Dynamic Phenomena (Proposal) +