Research map / Probabilistic and scientific modeling / Dynamical systems
Almost Surely Stable Deep Dynamics
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| enforce Lyapunov stability inside the model instead of a standard MDN | MDN dynamics model · nonlinear stochastic differential equation (Example 3) | negative log-likelihood over time | Helped |
| use implicit (non-convex Lyapunov) stability layer instead of convexity-based closed form (ablation) | stable dynamics model · Example 2 nonlinear system trajectories | trajectory fidelity | Helped |
Closest papers by meaning
- Learning Stable Deep Dynamics Models (NeurIPS 2019 · similarity 0.93)
- Learning Deep Input-Output Stable Dynamics (NeurIPS 2022 · similarity 0.89)
- Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems (NeurIPS 2021 · similarity 0.89)
- On the Stochastic Stability of Deep Markov Models (NeurIPS 2021 · similarity 0.88)
- Lyapunov-Stable Deep Equilibrium Models (AAAI 2024 · similarity 0.87)
- Deep learning for continuous-time stochastic control with jumps (NeurIPS 2025 · similarity 0.87)
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