A self-supervised PINN estimates human joint kinematics and kinetics from sparse IMU data by enforcing physical plausibility via a body model, reporting RMSD of 8.7 deg and 4.9 BWBH% on walking and running.
Kingma and Jimmy Ba
2 Pith papers cite this work. Polarity classification is still indexing.
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Physics-guided data augmentation combined with neural networks enables accurate indentation size effect correction in steels from small sets of shallow nanoindentation measurements, outperforming Nix-Gao in the shallow regime.
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SSPINNpose: A Self-Supervised PINN for Inertial Pose and Dynamics Estimation
A self-supervised PINN estimates human joint kinematics and kinetics from sparse IMU data by enforcing physical plausibility via a body model, reporting RMSD of 8.7 deg and 4.9 BWBH% on walking and running.
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Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation
Physics-guided data augmentation combined with neural networks enables accurate indentation size effect correction in steels from small sets of shallow nanoindentation measurements, outperforming Nix-Gao in the shallow regime.