Chebyshev polynomial surrogates enable one-shot closed-form adaptation of PINNs for a broader class of nonlinear ODEs and PDEs by decomposing them into linear subproblems.
L-hydra: Multi-head physics-informed neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Pi-PINN learns transferable physics-informed representations and solves known or unseen PDEs via closed-form pseudoinverse head adaptation, achieving 100-1000x faster predictions and 10-100x lower error than standard PINNs or data-driven models even with minimal training samples.
citing papers explorer
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Chebyshev-Augmented One-Shot Transfer Learning for PINNs on Nonlinear Differential Equations
Chebyshev polynomial surrogates enable one-shot closed-form adaptation of PINNs for a broader class of nonlinear ODEs and PDEs by decomposing them into linear subproblems.
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Transferable Physics-Informed Representations via Closed-Form Head Adaptation
Pi-PINN learns transferable physics-informed representations and solves known or unseen PDEs via closed-form pseudoinverse head adaptation, achieving 100-1000x faster predictions and 10-100x lower error than standard PINNs or data-driven models even with minimal training samples.