Physics-Informed Hemodynamic Modeling for Data-Free Prediction and Sparse-Data Assimilation

arXiv:2609.19290 · eess.IV, cs.AI · Submitted 2026-09-16 · Read on arXiv

eess.IV, cs.AI

Submitted: 2026-09-16

Updated: 2026-09-16

Code: https://github.com/xchenim/JacobiNet_bloodflow

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 46× in this paper · explained in 1 episode
• AUC — 11× in this paper · explained in 2 episodes
• Random Fourier Feature — 11× in this paper
• Robustness — 11× in this paper · explained in 5 episodes
• Transfer Learning — 10× in this paper · explained in 10 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Data Assimilation — 9× in this paper · explained in 2 episodes
• Core — 6× in this paper · explained in 2 episodes
• Reconstruction Error — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Physics-Informed Neural Networks (PINNs) — 3× in this paper · explained in 6 episodes
• Point Clouds — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Activation Function — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• DeepONet — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Imager Pixel Spacing — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Composite Loss Function — 1× in this paper · explained in 2 episodes
• Data Fusion — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Higher-order derivatives — 1× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 1× in this paper
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• PDE residual — 1× in this paper · explained in 2 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• What makes ImageNet good for transfer learning?
• On the choice of proper outlet boundary conditions for numerical simulation of cardiovascular flows

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