transfer files

experiments/config.yaml

@@ -20,7 +20,7 @@ trainer:
         mode: min
         patience: 10
         verbose: false
-  max_epochs: 200
+  max_epochs: 2000
   min_epochs: null
   max_steps: -1
   min_steps: null

experiments/config_01.yaml

@@ -9,8 +9,8 @@ trainer:
   logger:
     - class_path: lightning.pytorch.loggers.TensorBoardLogger
       init_args:
-        save_dir: lightning_logs
-        name: "01"
+        save_dir: logs
+        name: "test"
         version: null
   callbacks:
     - class_path: lightning.pytorch.callbacks.ModelCheckpoint
@@ -33,26 +33,21 @@ trainer:
   log_every_n_steps: null
   inference_mode: true
   default_root_dir: null
-  accumulate_grad_batches: 6
-  # gradient_clip_val: 1.0
+  accumulate_grad_batches: 4
+  gradient_clip_val: 1.0
 model: 
-  class_path: ThermalSolver.module.GraphSolver
+  class_path: ThermalSolver.graph_module.GraphSolver
   init_args:
-    model_class_path: ThermalSolver.model.local_gno.GatingGNO
+    model_class_path: ThermalSolver.model.LearnableGraphFiniteDifference
     model_init_args:
-      x_ch_node: 1 
-      f_ch_node: 1 
-      hidden: 16
-      layers: 1
-      edge_ch: 3 
-      out_ch: 1
+      max_iters: 250
     unrolling_steps: 64
 data: 
-  class_path: ThermalSolver.data_module.GraphDataModule
+  class_path: ThermalSolver.graph_datamodule.GraphDataModule
   init_args:
     hf_repo: "SISSAmathLab/thermal-conduction"
-    split_name: "2000_ref_1"
-    batch_size: 4
+    split_name: "1000_40x30"
+    batch_size: 8
     train_size: 0.8
     test_size: 0.1
     test_size: 0.1

experiments/config_64.yaml

@@ -33,11 +33,11 @@ trainer:
   log_every_n_steps: null
   inference_mode: true
   default_root_dir: null
-  accumulate_grad_batches: 6
+  accumulate_grad_batches: 2
   gradient_clip_val: 1.0
 
 model: 
-  class_path: ThermalSolver.module.GraphSolver
+  class_path: ThermalSolver.graph_module.GraphSolver
   init_args:
     model_class_path: ThermalSolver.model.local_gno.GatingGNO
     model_init_args:
@@ -49,11 +49,11 @@ model:
       out_ch: 1
     unrolling_steps: 1
 data: 
-  class_path: ThermalSolver.data_module.GraphDataModule
+  class_path: ThermalSolver.graph_datamodule.GraphDataModule
   init_args:
     hf_repo: "SISSAmathLab/thermal-conduction"
-    split_name: "2000"
-    batch_size: 4
+    split_name: "2000_ref_1"
+    batch_size: 10
     train_size: 0.8
     test_size: 0.1
     test_size: 0.1

experiments/config_autoregressive.yaml

@@ -0,0 +1,70 @@
+# lightning.pytorch==2.5.5
+seed_everything: 1999
+trainer:
+  accelerator: gpu
+  strategy: auto
+  devices: 1
+  num_nodes: 1
+  precision: null
+  logger:
+    - class_path: lightning.pytorch.loggers.TensorBoardLogger
+      init_args:
+        save_dir: logs.autoregressive
+        name: "test"
+        version: null
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+      init_args:
+        monitor: val/loss
+        mode: min
+        save_top_k: 1
+        filename: best-checkpoint
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: val/loss
+        mode: min
+        patience: 50
+        verbose: false
+  max_epochs: 1000
+  min_epochs: null
+  max_steps: -1
+  min_steps: null
+  overfit_batches: 0.0
+  log_every_n_steps: null
+  accumulate_grad_batches: 1
+  # reload_dataloaders_every_n_epochs: 1
+  default_root_dir: null
+
+model: 
+  class_path: ThermalSolver.autoregressive_module.GraphSolver
+  init_args:
+    model_class_path: ThermalSolver.model.learnable_finite_difference.CorrectionNet
+    model_init_args: 
+      input_dim: 1
+      hidden_dim: 24
+      # output_dim: 1
+      n_layers: 1
+    start_unrolling_steps: 1
+    increase_every: 100000
+    increase_rate: 2
+    max_inference_iters: 300
+    max_unrolling_steps: 40
+    inner_steps: 1
+
+data: 
+  class_path: ThermalSolver.graph_datamodule_unsteady.GraphDataModule
+  init_args:
+    hf_repo: "SISSAmathLab/thermal-conduction-unsteady"
+    split_name: "50_samples_easy"
+    batch_size: 64
+    train_size: 0.02
+    val_size: 0.02
+    test_size: 0.96
+    build_radial_graph: true
+    radius: 0.5
+    remove_boundary_edges: true
+    start_unrolling_steps: 1
+
+optimizer: null
+lr_scheduler: null
+# ckpt_path: logs/test/version_0/checkpoints/best-checkpoint.ckpt

experiments/config_fd.yaml

@@ -0,0 +1,58 @@
+# lightning.pytorch==2.5.5
+seed_everything: 1999
+trainer:
+  accelerator: gpu
+  strategy: auto
+  devices: 1
+  num_nodes: 1
+  precision: null
+  logger:
+    - class_path: lightning.pytorch.loggers.TensorBoardLogger
+      init_args:
+        save_dir: logs
+        name: "fd"
+        version: null
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+      init_args:
+        monitor: val/loss
+        mode: min
+        save_top_k: 1
+        filename: best-checkpoint
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: val/loss
+        mode: min
+        patience: 2
+        verbose: false
+  max_epochs: 1000
+  min_epochs: null
+  max_steps: -1
+  min_steps: null
+  overfit_batches: 0.0
+  log_every_n_steps: null
+  inference_mode: true
+  default_root_dir: null
+  accumulate_grad_batches: 4
+  gradient_clip_val: 1.0
+model: 
+  class_path: ThermalSolver.graph_module.GraphSolver
+  init_args:
+    model_class_path: ThermalSolver.model.GraphFiniteDifference
+    # model_init_args:
+    max_iters: 10000
+    # unrolling_steps: 64
+data: 
+  class_path: ThermalSolver.graph_datamodule.GraphDataModule
+  init_args:
+    hf_repo: "SISSAmathLab/thermal-conduction"
+    split_name: "1000_1_40x30"
+    batch_size: 8
+    train_size: 0.8
+    test_size: 0.1
+    test_size: 0.1
+    # build_radial_graph: true
+    # radius: 1.5
+optimizer: null
+lr_scheduler: null
+# ckpt_path: lightning_logs/01/version_0/checkpoints/best-checkpoint.ckpt

experiments/config_gino.yaml

@@ -0,0 +1,74 @@
+# lightning.pytorch==2.5.5
+seed_everything: 1999
+trainer:
+  accelerator: gpu
+  strategy: auto
+  devices: 1
+  num_nodes: 1
+  precision: null
+  logger:
+    - class_path: lightning.pytorch.loggers.TensorBoardLogger
+      init_args:
+        save_dir: logs
+        name: "test"
+        version: null
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+      init_args:
+        monitor: val/loss
+        mode: min
+        save_top_k: 1
+        filename: best-checkpoint
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: val/loss
+        mode: min
+        patience: 15
+        verbose: false
+  max_epochs: 1000
+  min_epochs: null
+  max_steps: -1
+  min_steps: null
+  overfit_batches: 0.0
+  log_every_n_steps: null
+  inference_mode: true
+  default_root_dir: null
+  # accumulate_grad_batches: 2
+  # gradient_clip_val: 1.0
+model: 
+  class_path: ThermalSolver.graph_module.GraphSolver
+  init_args:
+    model_class_path: neuralop.models import GINO
+    model_init_args:
+      in_channels: 3           # Es: coordinate (x, y, z) + valore della conducibilità k
+      out_channels: 1            # Es: temperatura T
+
+      # Parametri per l'encoder e il decoder GNO
+      gno_coord_features=3,      # Dimensionalità delle coordinate per GNO (es. 3D)
+      gno_n_layers=2,            # Numero di layer GNO nell'encoder e nel decoder
+      gno_hidden_channels=64,    # Canali nascosti per i layer GNO
+
+      # Parametri per il processore FNO
+      fno_n_modes=(16, 16, 16),  # Numero di modi di Fourier per ogni dimensione
+      fno_n_layers=4,            # Numero di layer FNO
+      fno_hidden_channels=64,    # Canali nascosti per i layer FNO
+      
+      # Canali per il lifting e la proiezione
+      lifting_channels=256,      # Dimensione dello spazio latente dopo il lifting iniziale
+      projection_channels=256,   # Dimensione prima della proiezione finale
+      
+      # Padding del dominio per il processore FNO
+      domain_padding=0.05
+    # unrolling_steps: 64
+data: 
+  class_path: ThermalSolver.graph_datamodule.GraphDataModule
+  init_args:
+    hf_repo: "SISSAmathLab/thermal-conduction"
+    split_name: "2000_ref_1"
+    batch_size: 64
+    train_size: 0.8
+    test_size: 0.1
+    test_size: 0.1
+optimizer: null
+lr_scheduler: null
+# ckpt_path: lightning_logs/01/version_0/checkpoints/best-checkpoint.ckpt

experiments/config_gno.yaml

@@ -44,12 +44,12 @@ model:
     increase_every: 10
     increase_rate: 2
     max_iters: 2000
-    accumulation_iters: 320
+    accumulation_iters: 160
 data: 
   class_path: ThermalSolver.graph_datamodule.GraphDataModule
   init_args:
     hf_repo: "SISSAmathLab/thermal-conduction"
-    split_name: "1000_40x30"
+    split_name: "1000_1_40x30"
     batch_size: 32
     train_size: 0.8
     test_size: 0.1

experiments/config_gno_inference.yaml

@@ -0,0 +1,62 @@
+# lightning.pytorch==2.5.5
+seed_everything: 1999
+trainer:
+  accelerator: gpu
+  strategy: auto
+  devices: 1
+  num_nodes: 1
+  precision: null
+  logger:
+    - class_path: lightning.pytorch.loggers.TensorBoardLogger
+      init_args:
+        save_dir: logs_inference
+        name: "test"
+        version: null
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+      init_args:
+        monitor: val/loss
+        mode: min
+        save_top_k: 1
+        filename: best-checkpoint
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: val/loss
+        mode: min
+        patience: 25
+        verbose: false
+  max_epochs: 1000
+  min_epochs: null
+  max_steps: -1
+  min_steps: null
+  overfit_batches: 0.0
+  log_every_n_steps: null
+  # inference_mode: true
+  default_root_dir: null
+  # accumulate_grad_batches: 2
+  # gradient_clip_val: 1.0
+model: 
+  class_path: ThermalSolver.graph_module.GraphSolver
+  init_args:
+    model_class_path: ThermalSolver.model.finite_difference.FiniteDifferenceStep
+    curriculum_learning: true
+    start_iters: 5
+    increase_every: 10
+    increase_rate: 2
+    max_iters: 2000
+    accumulation_iters: 320
+data: 
+  class_path: ThermalSolver.graph_datamodule.GraphDataModule
+  init_args:
+    hf_repo: "SISSAmathLab/thermal-conduction"
+    split_name: "1000_3_40x30"
+    batch_size: 10
+    train_size: 0.8
+    test_size: 0.1
+    test_size: 0.1
+    build_radial_graph: True
+    radius: 1.2
+    remove_boundary_edges: false
+optimizer: null
+lr_scheduler: null
+# ckpt_path: logs/test/version_2/checkpoints/best-checkpoint.ckpt

experiments/config_pointnet.yaml

@@ -0,0 +1,56 @@
+# lightning.pytorch==2.5.5
+seed_everything: 1999
+trainer:
+  accelerator: gpu
+  strategy: auto
+  devices: 1
+  num_nodes: 1
+  precision: null
+  logger:
+      - class_path: lightning.pytorch.loggers.TensorBoardLogger
+        init_args:
+          save_dir: lightning_logs
+          name: "pointnet"
+          version: null
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+      init_args:
+        monitor: val/loss
+        mode: min
+        save_top_k: 1
+        filename: best-checkpoint
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: val/loss
+        mode: min
+        patience: 10
+        verbose: false
+  max_epochs: 200
+  min_epochs: null
+  max_steps: -1
+  min_steps: null
+  overfit_batches: 0.0
+  log_every_n_steps: null
+  inference_mode: true
+  default_root_dir: null
+  accumulate_grad_batches: 2
+  gradient_clip_val: 1.0
+model: 
+  class_path: ThermalSolver.point_module.PointSolver
+  init_args:
+    model_class_path: ThermalSolver.model.point_net.PointNet
+    model_init_args:
+      input_dim: 4
+      output_dim: 1
+data: 
+  class_path: ThermalSolver.point_datamodule.PointDataModule
+  init_args:
+    hf_repo: "SISSAmathLab/thermal-conduction"
+    split_name: "2000"
+    batch_size: 10
+    train_size: 0.8
+    test_size: 0.1
+    test_size: 0.1
+optimizer: null
+lr_scheduler: null
+# ckpt_path: lightning_logs/pointnet/version_0/checkpoints/best-checkpoint.ckpt