arXiv (All)
open access
Online reduced-order modeling from primal solver trajectories for PDE-constrained optimization
Repeated high-fidelity state and gradient evaluations make large-scale PDE-constrained optimization computationally expensive. Reduced-order modeling provides a natural way to reduce the cost of these repeated high-dimensional computations. However, existing reduced-order modeling approaches often r
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Bound tightening in lifted formulations: (sub)solver-dependent impact on performance in RLT-based algorithms#1218335
- Coupled Adaptable Backward-Forward-Backward Resolvent Splitting Algorithm (CABRA): A Matrix-Parametrized Resolvent Splitting Method for the Sum of Maximal Monotone and Cocoercive Operators Composed with Linear Coupling Operators#1324467
- Convergence Analysis of Distributed Optimization: A Dissipativity Framework#1084072
- Improving Online Reinforcement Learning via Bidirectional Behavior Prior Distillation#1034036
- The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks#1291188
Record · ID 1354504
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive