Lines Matching refs:model

412 For explicit LES, it is defined by a subgrid stress model.
423   - Type of subgrid stress model to use. Currently only `data_driven` is available
428 (sgs-dd-model)=
431 The data-driven SGS model implemented here uses a small neural network to compute the SGS term.
433 More details regarding the theoretical background of the model can be found in {cite}`prakashDDSGS2…
445 The data-driven model parameters in the examples directory are not accurate and are for regression …
450 There are two different modes for using the data-driven model: fused and sequential.
452 In fused mode, the input processing, model inference, and output handling were all done in a single…
453 Fused mode is generally faster than the sequential mode, however fused mode requires that the model…
456 Sequential mode has separate function calls/CeedOperators for input creation, model inference, and …
457 …e three steps of the model evaluation, the sequential mode allows for functions calling external l…
458 …ies allows us to leverage the flexibility of those external libraries in their model architectures.
462 To specify the path to the PyTorch model file, use `-sgs_model_dd_torch_model_path`.
463 The hardware used to run the model inference is determined automatically from the libCEED backend c…
482   - Path to directory with data-driven model parameters (weights, biases, etc.)
487 …- Which computational implementation to use for SGS DD model (`fused`, `sequential_ceed`, `sequent…
492   - Path to the PyTorch `*.pt` file containing the DD inference model
497   - What hardware to perform the model inference on (`cpu`, `cuda`, `hip`, `xpu`)
540 The wavemode amplitudes $q^n$ are defined by a model energy spectrum $E(\kappa)$: