PI-NOMT: Physics-Informed Neural Optimal Mass Transport for Brain Fluid Dynamics

arXiv:2609.33857 · cs.LG, cs.AI · Submitted 2026-09-27 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Optimal Mass Transport — 57× in this paper
• Physics-Informed Neural Optimal Mass Transport — 48× in this paper
• Optimal Transport — 21× in this paper · explained in 5 episodes
• Regularization — 21× in this paper · explained in 3 episodes
• Consistency — 20× in this paper · explained in 3 episodes
• Ablation — 17× in this paper · explained in 1 episode
• PDE residual — 16× in this paper · explained in 2 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Inverse Problem — 7× in this paper · explained in 2 episodes
• Angular Error — 5× in this paper · explained in 1 episode
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Ablation Studies — 4× in this paper · explained in 1 episode
• Physical Constraints — 4× in this paper · explained in 7 episodes
• Conservation Laws — 2× in this paper · explained in 3 episodes
• DeepONet — 2× in this paper · explained in 2 episodes
• Physics-Informed Neural Networks (PINNs) — 2× in this paper · explained in 6 episodes
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Residual-based Attention (RBA) — 1× in this paper · explained in 2 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

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