Markovian Dynamics Enforcer: Feasibility Preserving Correction on Learned Dynamics Manifolds

arXiv:2609.39888 · cs.LG, cs.RO, cs.SY, eess.SY · Submitted 2026-09-30 · Read on arXiv

cs.LG, cs.RO, cs.SY, eess.SY

Submitted: 2026-09-30

Updated: 2026-10-01

Code: https://github.com/tsl-imperial/MaDE

Terminology

• Consistency — 23× in this paper · explained in 3 episodes
• Accuracy — 18× in this paper · explained in 1 episode
• Ablation — 13× in this paper · explained in 1 episode
• Transformer — 12× in this paper · explained in 2 episodes
• Steering — 10× in this paper · explained in 2 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 3× in this paper
• In International Conference on Machine Learning — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Admissibility — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Alternation — 1× in this paper · explained in 1 episode
• Closed-Loop Control — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Hybrid Models — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Improving Feasibility via Fast Autoencoder-Based Projections
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• Deep Kalman Filters
• Universal Differential Equations for Scientific Machine Learning
• Generative Adversarial Imitation from Observation

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