Ghost tasking for parametrized Gaussian Processes solving linear differential equations
Physics-informed machine learning has gained significant attention in recent years. In regimes of limited data, parametrized Gaussian processes have become popular. Existing approaches, however, often face limitations, such as requiring parametrizable (also called controllable) systems or a large number of output tasks. In this work, we introduce a systematic procedure we call "ghost tasking", using auxiliary tasks to circumvent these limitations. We prove that such ghost tasks can render any non-parametrizable system effectively parametrizable, enabling algorithmic construction of parametrized Gaussian Processes while keeping the number of required tasks (i.e. output dimensions) and late...
| Authors | Johanna Moser, Christopher Albert, Sascha Ranftl |
|---|---|
| Primary category | physics.comp-ph |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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