arXiv AI
Identifying ODEs from Unstructured Data with Causal Representation Learning
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arXiv Machine Learning
Jun 3
Learning Temporal Causal Structure via Smooth Differentiable Optimization
arXiv:2606. 03227v1 Announce Type: new Abstract: Causal discovery with instantaneous effects in multivariate time series is challenging, as the instantaneous structure must be acyclic.
arXiv Machine Learning
Jun 5
PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability
arXiv:2606. 05191v1 Announce Type: new Abstract: Data-driven equation discovery is fundamentally an inverse problem that seeks to infer the governing differential equations of a system directly from time-series measurements.
arXiv AI
Jun 17
FoundCause: Causal Discovery with Latent Confounders from Observational Data
arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.
arXiv Machine Learning
Jul 14
Discovering Latent Response Laws in Forced Physical Systems
arXiv:2607. 09801v1 Announce Type: new Abstract: Governing equations provide compact descriptions of physical systems, yet the variables in which they are simple are often hidden in high-dimensional measurements.
Hugging Face Trending Papers
Jun 10
How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit
Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering. While early approaches relied on empirical data and heuristics, modern data-driven methods offer greater flexibility and fewer assumptions.
arXiv Machine Learning
Jun 11
How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit
arXiv:2606. 12182v1 Announce Type: new Abstract: Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering.