HyperODE: Zero-Shot Surrogate for Simulation and Inference of Dynamical Systems
Understanding and controlling complex dynamical systems often requires executing thousands of numerical simulations across vast parametric landscapes, which is time-consuming. Machine learning surrogates significantly accelerate simulation by predicting state trajectories across different initializations and parameter values. However, surrogate models are specialized to one simulation model. Modifying the underlying differential equations - e.g., adding a physiological state or altering an epidemiological contact network - renders trained models obsolete and forces computationally expensive retraining from scratch. We introduce HyperODE, a surrogate capable of operating across an entire c...
| Authors | Ajitesh Srivastava |
|---|---|
| Primary category | cs.LG |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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