Physics-informed neural networks for two-dimensional wall-reactive solute dispersion in canonical shear flows

arXiv:2608.00856 · physics.flu-dyn, math-ph, physics.comp-ph, stat.ML · Submitted 2026-08-01 · Read on arXiv

Nanda Poddar, Subham Dhar

physics.flu-dyn, math-ph, physics.comp-ph, stat.ML

Submitted: 2026-08-01

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

Terminology

Benchmark — 21× in this paper · explained in 2 episodes
Accuracy — 20× in this paper · explained in 1 episode
Loss Function — 4× in this paper · explained in 2 episodes
Activation Function — 3× in this paper · explained in 1 episode
PDE residual — 3× in this paper · explained in 2 episodes
Physics-Informed Neural Network (PINN) — 3× in this paper · explained in 3 episodes
Adam — 2× in this paper · explained in 2 episodes
Composite Loss Function — 2× in this paper · explained in 2 episodes
Fidelity — 2× in this paper · explained in 2 episodes
Inverse Problem — 2× in this paper · explained in 2 episodes
Consistency — 1× in this paper · explained in 3 episodes
Diffusion Model — 1× in this paper · explained in 8 episodes
Fine-Tuning — 1× in this paper · explained in 5 episodes
Higher-order statistics — 1× in this paper · explained in 2 episodes
Homogenization — 1× in this paper · explained in 2 episodes
Initial Data — 1× in this paper · explained in 2 episodes
Physics-Informed Neural Networks (PINNs) — 1× in this paper · explained in 6 episodes
Robustness — 1× in this paper · explained in 5 episodes