Training-free score-based diffusion for parameter-dependent stochastic dynamical systems
This work addresses the problem of expensive simulations for parameter-dependent SDEs in fields like uncertainty quantification and real-time filtering, offering a more efficient solution.
The paper tackles the computational challenge of simulating parameter-dependent stochastic differential equations (SDEs) by introducing a training-free conditional diffusion model framework that approximates conditional score functions using trajectory data, enabling interpolation across state and parameter domains without retraining, and demonstrates accurate approximation in numerical examples.
Simulating parameter-dependent stochastic differential equations (SDEs) presents significant computational challenges, as separate high-fidelity simulations are typically required for each parameter value of interest. Despite the success of machine learning methods in learning SDE dynamics, existing approaches either require expensive neural network training for score function estimation or lack the ability to handle continuous parameter dependence. We present a training-free conditional diffusion model framework for learning stochastic flow maps of parameter-dependent SDEs, where both drift and diffusion coefficients depend on physical parameters. The key technical innovation is a joint kernel-weighted Monte Carlo estimator that approximates the conditional score function using trajectory data sampled at discrete parameter values, enabling interpolation across both state space and the continuous parameter domain. Once trained, the resulting generative model produces sample trajectories for any parameter value within the training range without retraining, significantly accelerating parameter studies, uncertainty quantification, and real-time filtering applications. The performance of the proposed approach is demonstrated via three numerical examples of increasing complexity, showing accurate approximation of conditional distributions across varying parameter values.