DGAE is a new inductive graph model using directed DEFP, latent encoding, and physics-guided pattern-specific propagation to outperform prior methods on sparse-sensor freeway traffic estimation.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2025 2verdicts
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Fatigue-PINN applies physics-informed neural networks to simulate fatigue effects on human motion using a three-compartment muscle model for joint torque modulation in motion synthesis.
citing papers explorer
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Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approach
DGAE is a new inductive graph model using directed DEFP, latent encoding, and physics-guided pattern-specific propagation to outperform prior methods on sparse-sensor freeway traffic estimation.
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Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis
Fatigue-PINN applies physics-informed neural networks to simulate fatigue effects on human motion using a three-compartment muscle model for joint torque modulation in motion synthesis.