Learning when to refine: long-horizon reinforcement learning for budgeted neural-operator PDE solvers
Read the original at arxiv.org→arXiv:2610.06883v1 Announce Type: new Abstract: Neural operators provide fast surrogates for time-dependent PDEs, but autoregressive deployment creates a refinement-allocation problem: prediction errors vary over...
Original headline: "Learning When to Refine: Long-Horizon Reinforcement Learning for Budgeted Neural-Operator PDE Solvers"
Coverage timeline
- Oct 7, 04:00 UTC arXiv cs.LG lead source Learning When to Refine: Long-Horizon Reinforcement Learning for Budgeted Neural-Operator PDE Solvers
- Oct 7, 04:00 UTC arXiv cs.LG Do Neural PDE Solvers Learn the Right Dynamics?