NVIDIA
diff --git a/‎.importlinter‎
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diff --git a/‎.pre-commit-config.yaml‎
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diff --git a/‎examples/structural_mechanics/crash/README.md‎
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diff --git a/‎examples/structural_mechanics/crash/conf/model/figconvunet_autoregressive_rollout_training.yaml‎
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diff --git a/‎examples/structural_mechanics/crash/conf/model/figconvunet_one_step_rollout.yaml‎
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diff --git a/‎examples/structural_mechanics/crash/conf/model/figconvunet_time_conditional.yaml‎
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diff --git a/‎examples/structural_mechanics/crash/rollout.py‎
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@@ -98,3 +98,9 @@ name = Prevent Non-listed external imports in physicsnemo nn
 type = forbidden_import
 container = physicsnemo.nn
 dependency_group = nn
+
+[importlinter:contract:physicsnemo-models-external-imports]
+name = Prevent Non-listed external imports in physicsnemo models
+type = forbidden_import
+container = physicsnemo.models
+dependency_group = models
@@ -54,7 +54,9 @@ repos:
     - id: check-added-large-files
       args: [--maxkb=5000]
 
--   repo: https://github.com/seddonym/import-linter
-    rev: v2.5.2
-    hooks:
-    - id: import-linter
+# This should be enabled once all dependencies are cleared.
+# For now, check status with `lint-imports`
+# -   repo: https://github.com/seddonym/import-linter
+#     rev: v2.5.2
+#     hooks:
+#     - id: import-linter
@@ -19,14 +19,14 @@ For an in-depth comparison between the Transolver and MeshGraphNet models and th
 
 <p align="center">
   <img src="../../../docs/img/crash/crash_case4_reduced.gif" alt="Crash animation" width="80%" />
-  
+
 </p>
 
 ### Crushcan Modeling
 
 <p align="center">
   <img src="../../../docs/img/crash/crushcan.gif" alt="Crushcan animation" width="80%" />
-  
+
 </p>
 
 ## Quickstart
@@ -238,7 +238,10 @@ conf/
 │   ├── mgn_time_conditional.yaml
 │   ├── transolver_autoregressive_rollout_training.yaml
 │   ├── transolver_one_step_rollout.yaml
-│   └── transolver_time_conditional.yaml
+│   ├── transolver_time_conditional.yaml
+│   ├── figconvunet_autoregressive_rollout_training.yaml
+│   ├── figconvunet_one_step_rollout.yaml
+│   └── figconvunet_time_conditional.yaml
 ├── training/default.yaml    # training hyperparameters
 └── inference/default.yaml   # inference options
 ```
@@ -495,7 +498,7 @@ run_post_processing.sh can automate all evaluation tasks across runs.
 
 - AMP is enabled by default in training; it reduces memory and accelerates matmuls on modern GPUs.
 - For multi-GPU training, use `torchrun --standalone --nproc_per_node=<NUM_GPUS> train.py`.
-- For DDP, prefer `torchrun --standalone --nproc_per_node=<NUM_GPUS> train.py`. 
+- For DDP, prefer `torchrun --standalone --nproc_per_node=<NUM_GPUS> train.py`.
 
 ## Troubleshooting / FAQ
 
 
@@ -0,0 +1,66 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+_target_: rollout.FIGConvUNetAutoregressiveRolloutTraining
+_convert_: all
+
+# Input/output channels
+in_channels: 5                       # velocity(3) + features(F) + time(1)
+out_channels: 3                      # acceleration (xyz)
+
+# Architecture
+kernel_size: 3
+hidden_channels: [16, 16, 16]       # channels at each level
+num_levels: 2                        # number of down/up levels
+num_down_blocks: 1
+num_up_blocks: 1
+mlp_channels: [256, 256]
+
+# Spatial domain
+aabb_max: [2.0, 2.0, 2.0]
+aabb_min: [-2.0, -2.0, -2.0]
+voxel_size: null
+
+# Grid resolutions (factorized implicit grids)
+# Format: [memory_format, resolution_tuple]
+resolution_memory_format_pairs:
+  - [b_xc_y_z, [2, 64, 64]]
+  - [b_yc_x_z, [64, 2, 64]]
+  - [b_zc_x_y, [64, 64, 2]]
+
+# Position encoding
+use_rel_pos: true
+use_rel_pos_embed: true
+pos_encode_dim: 16
+
+# Communication and sampling
+communication_types: ["sum"]
+to_point_sample_method: "graphconv"
+neighbor_search_type: "knn"
+knn_k: 16
+reductions: ["mean"]
+
+use_scalar_output: false
+has_input_features: true
+
+# Pooling (for global features if needed)
+pooling_type: "max"
+pooling_layers: [2]
+
+# Rollout parameters
+num_time_steps: ${training.num_time_steps}
+dt: 5e-3
+initial_vel: 9.22
@@ -0,0 +1,66 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+_target_: rollout.FIGConvUNetOneStepRollout
+_convert_: all
+
+# Input/output channels
+in_channels: 4                       # velocity(3) + features (F)
+out_channels: 3                      # next step position (xyz)
+
+# Architecture
+kernel_size: 3
+hidden_channels: [16, 16, 16]        # channels at each level
+num_levels: 2                        # number of down/up levels
+num_down_blocks: 1
+num_up_blocks: 1
+mlp_channels: [256, 256]
+
+# Spatial domain
+aabb_max: [2.0, 2.0, 2.0]
+aabb_min: [-2.0, -2.0, -2.0]
+voxel_size: null
+
+# Grid resolutions (factorized implicit grids)
+# Format: Uses res_mem_pair resolver (memory_format_enum, resolution_tuple)
+resolution_memory_format_pairs:
+  - [b_xc_y_z, [2, 64, 64]]
+  - [b_yc_x_z, [64, 2, 64]]
+  - [b_zc_x_y, [64, 64, 2]]
+
+# Position encoding
+use_rel_pos: true
+use_rel_pos_embed: true
+pos_encode_dim: 16
+
+# Communication and sampling
+communication_types: ["sum"]
+to_point_sample_method: "graphconv"
+neighbor_search_type: "knn"
+knn_k: 16
+reductions: ["mean"]
+
+use_scalar_output: false
+has_input_features: true
+
+# Pooling (for global features if needed)
+pooling_type: "max"
+pooling_layers: [2]
+
+# Rollout parameters
+num_time_steps: ${training.num_time_steps}
+dt: 5e-3
+initial_vel: 9.22
@@ -53,6 +53,9 @@ neighbor_search_type: "knn"
 knn_k: 16
 reductions: ["mean"]
 
+use_scalar_output: false
+has_input_features: true
+
 # Pooling (for global features if needed)
 pooling_type: "max"
 pooling_layers: [2]
 
@@ -467,3 +467,171 @@ def step_fn(verts, feats):
             outputs.append(y_t)
 
         return torch.stack(outputs, dim=0)  # [T, N, 3]
+
+
+class FIGConvUNetOneStepRollout(FIGConvUNet):
+    """
+    FIGConvUNet with one-step rollout for crash simulation.
+
+    - Training: teacher forcing (uses GT positions at each step)
+    - Inference: autoregressive (uses predictions)
+    """
+
+    def __init__(self, *args, **kwargs):
+        self.dt: float = kwargs.pop("dt", 5e-3)
+        self.initial_vel: torch.Tensor = kwargs.pop("initial_vel")
+        self.rollout_steps: int = kwargs.pop("num_time_steps") - 1
+        super().__init__(*args, **kwargs)
+
+    def forward(self, sample: SimSample, data_stats: dict) -> torch.Tensor:
+        """
+        Args:
+            Sample: SimSample containing node_features and node_target
+            data_stats: dict containing normalization stats
+        Returns:
+            [T, N, 3] rollout of predicted positions
+        """
+        inputs = sample.node_features
+        x0 = inputs["coords"]  # initial pos [N, 3]
+        features = inputs.get("features", x0.new_zeros((x0.size(0), 0)))  # [N, F]
+
+        # Ground truth sequence [T, N, 3]
+        N = x0.size(0)
+        gt_seq = torch.cat(
+            [x0.unsqueeze(0), sample.node_target.view(N, -1, 3).transpose(0, 1)],
+            dim=0,
+        )
+
+        outputs: list[torch.Tensor] = []
+        # First step: backstep to create y_-1
+        y_t0 = gt_seq[0] - self.initial_vel * self.dt
+        y_t1 = gt_seq[0]
+
+        for t in range(self.rollout_steps):
+            # In training mode (except first step), use ground truth positions
+            if self.training and t > 0:
+                y_t0, y_t1 = gt_seq[t - 1], gt_seq[t]
+
+            # Prepare vertices for FIGConvUNet: [1, N, 3]
+            vertices = y_t1.unsqueeze(0)  # [1, N, 3]
+
+            vel = (y_t1 - y_t0) / self.dt
+            vel_norm = (vel - data_stats["node"]["norm_vel_mean"]) / (
+                data_stats["node"]["norm_vel_std"] + EPS
+            )
+
+            # [1, N, 3 + F]
+            fx_t = torch.cat([vel_norm, features], dim=-1).unsqueeze(0)
+
+            def step_fn(verts, feats):
+                out, _ = super(FIGConvUNetOneStepRollout, self).forward(
+                    vertices=verts, features=feats
+                )
+                return out
+
+            if self.training:
+                outf = ckpt(
+                    step_fn,
+                    vertices,
+                    fx_t,
+                    use_reentrant=False,
+                ).squeeze(0)  # [N, 3]
+            else:
+                outf = step_fn(vertices, fx_t).squeeze(0)  # [N, 3]
+
+            acc = (
+                outf * data_stats["node"]["norm_acc_std"]
+                + data_stats["node"]["norm_acc_mean"]
+            )
+            vel_pred = self.dt * acc + vel
+            y_t2_pred = self.dt * vel_pred + y_t1
+
+            outputs.append(y_t2_pred)
+
+            if not self.training:
+                # autoregressive update for inference
+                y_t0, y_t1 = y_t1, y_t2_pred
+
+        return torch.stack(outputs, dim=0)  # [T, N, 3]
+
+
+class FIGConvUNetAutoregressiveRolloutTraining(FIGConvUNet):
+    """
+    FIGConvUNet with autoregressive rollout training for crash simulation.
+
+    Predicts sequence by autoregressively updating velocity and position
+    using predicted accelerations. Supports gradient checkpointing during training.
+    """
+
+    def __init__(self, *args, **kwargs):
+        self.dt: float = kwargs.pop("dt")
+        self.initial_vel: torch.Tensor = kwargs.pop("initial_vel")
+        self.rollout_steps: int = kwargs.pop("num_time_steps") - 1
+        super().__init__(*args, **kwargs)
+
+    def forward(self, sample: SimSample, data_stats: dict) -> torch.Tensor:
+        """
+        Args:
+            sample: SimSample containing node_features and node_target
+            data_stats: dict containing normalization stats
+        Returns:
+            [T, N, 3] rollout of predicted positions
+        """
+        inputs = sample.node_features
+        coords = inputs["coords"]  # [N, 3]
+        features = inputs.get("features", coords.new_zeros((coords.size(0), 0)))
+        N = coords.size(0)
+        device = coords.device
+
+        # Initial states
+        y_t1 = coords  # [N, 3]
+        y_t0 = y_t1 - self.initial_vel * self.dt  # backstep using initial velocity
+
+        outputs: list[torch.Tensor] = []
+        for t in range(self.rollout_steps):
+            time_t = 0.0 if self.rollout_steps <= 1 else t / (self.rollout_steps - 1)
+            time_t = torch.tensor([time_t], device=device, dtype=torch.float32)
+
+            # Velocity normalization
+            vel = (y_t1 - y_t0) / self.dt
+            vel_norm = (vel - data_stats["node"]["norm_vel_mean"]) / (
+                data_stats["node"]["norm_vel_std"] + EPS
+            )
+
+            # Prepare vertices for FIGConvUNet: [1, N, 3]
+            vertices = y_t1.unsqueeze(0)  # [1, N, 3]
+
+            # Prepare features: vel_norm + features + time [N, 3+F+1]
+            fx_t = torch.cat(
+                [vel_norm, features, time_t.expand(N, 1)], dim=-1
+            )  # [N, 3+F+1]
+            fx_t = fx_t.unsqueeze(0)  # [1, N, 3+F+1]
+
+            def step_fn(verts, feats):
+                out, _ = super(FIGConvUNetAutoregressiveRolloutTraining, self).forward(
+                    vertices=verts, features=feats
+                )
+                return out
+
+            if self.training:
+                outf = ckpt(
+                    step_fn,
+                    vertices,
+                    fx_t,
+                    use_reentrant=False,
+                ).squeeze(0)  # [N, 3]
+            else:
+                outf = step_fn(vertices, fx_t).squeeze(0)  # [N, 3]
+
+            # De-normalize acceleration
+            acc = (
+                outf * data_stats["node"]["norm_acc_std"]
+                + data_stats["node"]["norm_acc_mean"]
+            )
+            vel = self.dt * acc + vel
+            y_t2 = self.dt * vel + y_t1
+
+            outputs.append(y_t2)
+            y_t1, y_t0 = y_t2, y_t1
+
+        return torch.stack(outputs, dim=0)  # [T, N, 3]