A PINN embedding Cosserat rod mechanics achieves sub-1% mean shape error for 6-DoF concentric tube robots using minimal training data and outperforms a pure physics baseline.
Understanding and mitigating gradient flow pathologies in physics-informed neural networks,
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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.
SOLIS recovers interpretable physical parameters for nonlinear systems by learning state-conditioned Quasi-LPV neural surrogates without assuming a fixed global equation.
citing papers explorer
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Few-Shot Physics-Informed Neural Network for Shape Reconstruction of Concentric-Tube Robots
A PINN embedding Cosserat rod mechanics achieves sub-1% mean shape error for 6-DoF concentric tube robots using minimal training data and outperforms a pure physics baseline.
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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.
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SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
SOLIS recovers interpretable physical parameters for nonlinear systems by learning state-conditioned Quasi-LPV neural surrogates without assuming a fixed global equation.