Staged closure learning offers efficiency gains
Original title: Efficient Closure Learning in Dynamical Systems: A Comparison of Data-Assimilated and Embedded Approaches
Staged approach matches embedded accuracy with fewer evaluations in closure modeling.
Closure models approximate unresolved effects in dynamical systems. Embedded approaches train closures inside simulators but risk instabilities and high cost. Staged approaches use data assimilation then offline training. For SIQR epidemiological model, embedded training gives strongest accuracy while staged yields comparable behavior with substantially fewer evaluations and better optimization robustness.
- Staged approach reduces model evaluations
- Comparable qualitative behavior
- Improved optimization robustness
- Moderate increase in error
From the original article
1 min read at arxiv.orgIncreased computing power has transformed computational science and improved predictive capabilities for complex systems, but many phenomena still exceed current simulation capabilities or require rapid evaluations for fast design iterations. Closure models address this challenge by approximating unresolved effects in reduced or incomplete dynamical systems.
By Rileigh Bandy · arxiv.org
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