arXiv Machine Learning June 18, 2026 By Michael Detzel, Gabriel Nobis, Kristiyan Blagov, Juri Schubert, Jackie Ma, Wojciech Samek

INDEQS: Informed Neural controlled Differential EQuationS

Read the original on arXiv Machine Learning →

arXiv:2606. 19138v1 Announce Type: new Abstract: Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatial structure purely from data, even in settings where a directed graph structure is known a priori.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting

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