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直接匹配不多,已補上最新動態。

Tag: #pde-solving2 results

LAM-PINN 提升 PINNs 抗任務異質性效能

LAM-PINN 提升 PINNs 抗任務異質性效能

LAM-PINN 提出組合式元學習,解決物理資訊神經網路 (PINNs) 在參數化 PDE 中的任務異質性問題。它使用 PDE 參數及簡短轉移會話的學習親和度指標來聚類任務,將模型分解為專屬子網路並學習路由權重。僅用傳統 PINNs 10% 訓練迭代,即在未見任務上實現 19.7 倍 MSE 降低。

NMIPS: Neuro-Symbolic PDE Solver

NMIPS: Neuro-Symbolic PDE Solver

NMIPS introduces a unified neuro-symbolic framework for solving PDE families with shared structures but varying parameters. It discovers interpretable analytical solutions via multifactorial optimization and affine transfer for efficiency. Experiments show up to 35.7% accuracy gains over baselines.

ArXiv AIResearchFeb 13#research#arxiv#nmips