| Symbol | Meaning |
|---|---|
| matrix | |
| learning rate or step size | |
| boundary of computational domain |
|
| generic function to be approximated, typically unknown | |
| approximate version of |
|
| computational domain | |
| continuous/ideal physical model | |
| discretized physical model, PDE | |
| neural network params | |
| time dimension | |
| vector-valued velocity | |
| neural network input or spatial coordinate | |
| neural network output | |
| learning targets: ground truth, reference or observation data |
| Abbreviation | Meaning |
|---|---|
| AI | Mysterious buzzword popping up in all kinds of places these days |
| BNN | Bayesian neural network |
| CNN | Convolutional neural network (specific NN architecure) |
| DDPM | Denoising diffusion probabilistic models (diffusion modeling variant) |
| DL | Deep Learning |
| FM | Flow matching (diffusion modeling variant) |
| FNO | Fourier neural operator (specific NN architecure) |
| GD | (steepest) Gradient Descent |
| MLP | Multi-Layer Perceptron, a neural network with fully connected layers |
| NN | Neural network (a generic one, in contrast to, e.g., a CNN or MLP) |
| PDE | Partial Differential Equation |
| PBDL | Physics-Based Deep Learning |
| SGD | Stochastic Gradient Descent |