ニューラルオペレーターと古典的手法を融合するための貪欲なPDEルーター
原題: A Greedy PDE Router for Blending Neural Operators and Classical Methods
分析結果
- カテゴリ
- エネルギー
- 重要度
- 68
- トレンドスコア
- 27
- 要約
- 偏微分方程式（PDE）を解く際、古典的な数値解法は計算コストが高くなることが多い。一方、機械学習手法はスペクトルバイアスに悩まされ、解の特性を捉えられないことがある。本研究では、これらの問題を解決するために、ニューラルオペレーターと古典的手法を組み合わせた新しいアプローチを提案する。
- キーワード
arXiv:2509.24814v2 Announce Type: replace-cross Abstract: When solving PDEs, classical numerical solvers are often computationally expensive, while machine learning methods can suffer from spectral bias, failing to capture high-frequency components. Designing an optimal hybrid iterative solver--where, at each iteration, a solver is selected from an ensemble of solvers to leverage their complementary strengths--poses a challenging combinatorial problem. While greedy selection is desirable for its constant-factor approximation guarantee to the optimal solution under Lipschitz assumptions, it requires knowledge of the true error at each step, which is unavailable in practice. We address this by proposing an approximate greedy router that efficiently mimics a greedy approach to solver selection. Empirical results on the Poisson and convection-diffusion equations show that our method consistently reduces final error and area-under-the-curve (AUC) of the error trajectory relative to single-solver baselines and existing hybrid approaches such as HINTS. In particular, our method reaches comparable error levels in substantially fewer iterations while exhibiting more stable error decay. arXiv:2509.24814v2 Announce Type: replace-cross Abstract: When solving PDEs, classical numerical solvers are often computationally expensive, while machine learning methods can suffer from spectral bias, failing to capture high-frequency components. Designing an optimal hybrid iterative solver--where, at each iteration, a solver is selected from an ensemble of solvers to leverage their complementary strengths--poses a challenging combinatorial problem. While greedy selection is desirable for its constant-factor approximation guarantee to the optimal solution under Lipschitz assumptions, it requires knowledge of the true error at each step, which is unavailable in practice. We address this by proposing an approximate greedy router that efficiently mimics a greedy approach to solver selection. Empirical results on the Poisson and convection-diffusion equations show that our method consistently reduces final error and area-under-the-curve (AUC) of the error trajectory relative to single-solver baselines and existing hybrid approaches such as HINTS. In particular, our method reaches comparable error levels in substantially fewer iterations while exhibiting more stable error decay.