iPool--Information-Based Pooling in Hierarchical Graph Neural Networks

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The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

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We prove the non-planarity of a family of 3-regular graphs constructed from the solutions to the Markoff equation x2 + y2 + z2 = xyz modulo prime numbers greater than 7. The proof uses Euler characteristic and an enumeration of the short cycles in these gr ...

iPool--Information-Based Pooling in Hierarchical Graph Neural Networks

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A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Machine Learning for Modeling Stock Returns

Network-based kinetic models: Emergence of a statistical description of the graph topology

Optimizing Dynamic Aperture Studies with Active Learning

Molecular hypergraph neural networks

In Medio Stat Virtus: Combining Boolean and Pattern Matching

Topics in statistical physics of high-dimensional machine learning

Non-planarity of Markoff graphs mod p