| name | dysco-latent-dynamics-extraction |
| description | DYSCO (Dynamics via Contrastive Learning) - 多视角对比学习从噪声观测中提取潜在动力学系统。通过独立噪声视角分离信号与噪声，恢复潜在轨迹和支配动力学方程。 |
| author | Paolo Muratore, Mackenzie Weygandt Mathis |
| arxiv_id | 2606.1326 |
| categories | ["machine-learning","dynamical-systems","neuroscience","system-identification"] |
| tags | ["contrastive-learning","latent-dynamics","system-identification","multi-view-learning","governing-equations","neural-dynamics","symbolic-discovery"] |
| created | 2026-06-15T00:00:00.000Z |
| source | arXiv cs.LG/q-bio.NC |
DYSCO: Extracting Governing Equations from Latent Dynamics
Overview
DYSCO (DYnamics via Contrastive Learning) is a multi-view temporal contrastive learning algorithm that jointly recovers latent trajectories and governing dynamics from noisy, high-dimensional measurements. It leverages multiple independent noisy views of the same underlying process to disentangle signal from noise.
Key Innovation
Multi-View Contrastive Framework
- Uses independent noisy views of same underlying process
- Separates signal from noise via contrastive learning
- Recovers latent trajectories AND governing dynamics simultaneously
- Symbolic recovery of governing equations in affine gauge
Problem Setting
Input: Noisy, high-dimensional observations
Challenge:
- Short recordings
- Noisy measurements
- Coarsely sampled
- Autocorrelated structure (e.g., fMRI)
Output:
- Latent trajectories (signal)
- Governing equations (symbolic)
- Flow fields (dynamics)
Core Methodology
1. Multi-View Architecture
Views: Multiple independent noisy observations
y₁(t) = g₁(x(t)) + noise₁
y₂(t) = g₂(x(t)) + noise₂
...
Contrastive objective:
- Align views of same latent state
- Separate noise through independence
2. Temporal Contrastive Learning
- Temporal pairs: Same time point, different views
- Contrastive loss: Maximize agreement between views
- Noise separation: Independent noise → divergent representations
3. Functional Basis Parameterization
- Dynamics parameterized in structured functional basis
- Enables symbolic recovery of governing equations
- Affine gauge freedom allows interpretation
Theoretical Guarantees
Identifiability Results
- Strong identification up to affine indeterminacy
- Extends prior identifiability results to noisy nonlinear observations
- Guarantees for both Gaussian and Poisson noise
Key Conditions
- Multiple independent noisy views
- Weak coupling assumptions
- Structured functional basis
Mathematical Framework
Latent Dynamics Model
$$\dot{x} = f(x)$$
where:
