Revert "Update geotransolver for 2d and 3d use cases"

examples/cfd/darcy_transolver/README.md

@@ -89,15 +89,6 @@ To train Transolver on your own physics problem, modify the `dataloader` in
 `train_transolver_darcy.py` to use your own pre-computed data or on-the-fly
 solver.
 
-#### Other models
-
-You can also use GeoTransolver, Flare, or GeoTransolver with flare attention
-for the darcy example with the "fixed" usage.  Change the `model` parameter
-to update it!  Note that the inputs to geotransolver here uses the positions
-and the `x` diffusivity to both the model trunk and encoder paths.  For these
-problems, GeoTransolver is not yet extensively tested, and your results
-may vary.
-
 ## Additional Information
 
 More components are added for convenience. `Validators` calculate the loss

examples/cfd/darcy_transolver/config_fix.yaml

@@ -20,31 +20,33 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-defaults:
-  - model: transolver   # transolver | flare | geotransolver | geotransolver_fa
-  - _self_
-
-hydra:
-  output_subdir: null
-  run:
-    dir: .
-
-output_dir: ./output/
-run_id: ${hydra:runtime.choices.model}-muon_${precision}_r${resolution}_b${data.batch_size}_s${model.slice_num}
-
-resolution: 85 #421, 211, 141, 106, 85 all viable
+output_dir: ./output/darcy_transolver_fix
+run_id: bf16_dev_r85_b8_s64
 
 data:
-  train_path: //lustre/fsw/portfolios/coreai/users/coreya/datasets/darcy_fix/example_data/piececonst_r421_N1024_smooth1.npz
-  test_path: //lustre/fsw/portfolios/coreai/users/coreya/datasets/darcy_fix/example_data/piececonst_r421_N1024_smooth2.npz
-  resolution: ${resolution}
-  batch_size: 64 # This is the GLOBAL batch size
+  train_path: /user_data/datasets/darcy_fix/example_data/piececonst_r421_N1024_smooth1.npz
+  test_path: /user_data/datasets/darcy_fix/example_data/piececonst_r421_N1024_smooth2.npz
+  resolution: 85 #421, 211, 141, 106, 85 all viable
+  batch_size: 8 # This is the GLOBAL batch size
 
+model:
+  functional_dim: 1
+  out_dim: 1
+  embedding_dim: 2
+  n_layers: 4
+  n_hidden: 128
+  dropout: 0.0
+  n_head: 4
+  act: gelu
+  mlp_ratio: 4
+  unified_pos: False
+  ref: 8
+  slice_num: 64
+  use_te: False
+  time_input: False
+
 precision: bf16
 
-optimizer:
-  type: muon   # adamw | muon (muon requires PyTorch >= 2.9)
-
 normaliser:
   permeability:
     mean: 1.25
@@ -67,4 +69,3 @@ training:
 validation:
   sample_size: 200
   validation_pseudo_epochs: 1
-  save_plots: false