Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems

arXiv:2609.37944 · stat.ML, cs.LG, math.DS · Submitted 2026-09-29 · Read on arXiv

stat.ML, cs.LG, math.DS

Submitted: 2026-09-29

Updated: 2026-10-01

Terminology

• Benchmark — 29× in this paper · explained in 2 episodes
• Sparsity — 18× in this paper · explained in 4 episodes
• Causal Discovery — 11× in this paper · explained in 2 episodes
• Consistency — 9× in this paper · explained in 3 episodes
• Regularization — 6× in this paper · explained in 3 episodes
• Feature Selection — 4× in this paper · explained in 4 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Generative AI — 3× in this paper · explained in 2 episodes
• Activation Function — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Neural Granger Causality — 2× in this paper
• Symbolic Regression — 2× in this paper · explained in 5 episodes
• Causal Inference — 1× in this paper · explained in 4 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Processes — 1× in this paper · explained in 2 episodes
• Hamming Distance — 1× in this paper · explained in 2 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Non-Gaussianity — 1× in this paper · explained in 2 episodes
• Ordinary Differential Equations (ODEs) — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Temporal Causal Discovery Framework — 1× in this paper
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• Beyond Predictions in Neural ODEs: Identification and Interventions
• Adam: A Method for Stochastic Optimization
• Gradients are Not All You Need

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