Výsledky vyhledávání - "Loon, E. Emiel"

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Towards detailed and interpretable hybrid modeling of continental-scale bird migration

Autor: Lippert, Fiona, Kranstauber, Bart, Forré, Patrick, van Loon, E. Emiel

Publikováno v: AI for Science Workshop at the 41st International Conference on Machine Learning (ICML), 2024

Hybrid modeling aims to augment traditional theory-driven models with machine learning components that learn unknown parameters, sub-models or correction terms from data. In this work, we build on FluxRGNN, a recently developed hybrid model of contin

Externí odkaz: http://arxiv.org/abs/2407.10259

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Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems

Autor: Lippert, Fiona, Kranstauber, Bart, van Loon, E. Emiel, Forré, Patrick

Publikováno v: Advances in Neural Information Processing Systems 36, 2023

Probabilistic inference in high-dimensional state-space models is computationally challenging. For many spatiotemporal systems, however, prior knowledge about the dependency structure of state variables is available. We leverage this structure to dev

Externí odkaz: http://arxiv.org/abs/2306.08445

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Physics-informed inference of aerial animal movements from weather radar data

Autor: Lippert, Fiona, Kranstauber, Bart, van Loon, E. Emiel, Forré, Patrick

Publikováno v: AI for Science Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS), 2022

Studying animal movements is essential for effective wildlife conservation and conflict mitigation. For aerial movements, operational weather radars have become an indispensable data source in this respect. However, partial measurements, incomplete s

Externí odkaz: http://arxiv.org/abs/2211.04539

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