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pro vyhledávání: '"Nather, Rüdiger"'
Conservation laws are of great theoretical and practical interest. We describe a novel approach to machine learning conservation laws of finite-dimensional dynamical systems using trajectory data. It is the first such approach based on kernel methods
Externí odkaz:
http://arxiv.org/abs/2405.20857
Autor:
Moallemy-Oureh, Alice, Beddar-Wiesing, Silvia, Nagel, Yannick, Nather, Rüdiger, Thomas, Josephine M.
Temporal Point Processes (TPPs) have recently become increasingly interesting for learning dynamics in graph data. A reason for this is that learning on dynamic graph data is becoming more relevant, since data from many scientific fields, ranging fro
Externí odkaz:
http://arxiv.org/abs/2206.03469
Graphs can have different properties that lead to several graph types and may allow for a varying representation of diverse information. In order to clarify the modeling power of graphs, we introduce a partial order on the most common graph types bas
Externí odkaz:
http://arxiv.org/abs/2109.10708
Dynamic Graph Neural Networks recently became more and more important as graphs from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are dynamic by nature. While temporal changes (dynamics)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9204af20c73d69fc7ddb00bb5a482deb