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Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach

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arXiv:2606. 24966v1 Announce Type: new Abstract: Estimating parameters of dynamical systems from sparse, noisy, and irregularly sampled data is often severely ill-conditioned.

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Authors: Cristian Brugnara, Lea Multerer, Marco Forgione, Laura Azzimonti
Released: 2026-06-25 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach overlaps with experiment "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.24966

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