Noseong Park, Jeongwhan Choi, Kookjin Lee, Youn-Yeol Yu, Woojin Cho, Nayong Kim, Kiseok Chang, Chang-Seung Woo, Ilho Kim, Seok-Woo Lee, Joon-Young Yang, Sooyoung Yoon
We lifted 17 functions out of this paper's own repositories and ran 12 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| google-deepmind/deepmind-research | canonical | 7 of 11 |
| yuyudeep/hcmt | canonical | 5 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| add_targets | Ran | yuyudeep/hcmt/dataset.py pointer only (licence: NONE) · get_code("0f74786d92f7b6a0") |
| build_crops_biases | Ran | google-deepmind/deepmind-research/alphafold_casp13/contacts_network.py code served (permissive licence) · get_code("c2695f5eb1e963a3") |
| call_on_tuple | Ran | google-deepmind/deepmind-research/alphafold_casp13/contacts_network.py code served (permissive licence) · get_code("2a0b71f3e9b5a0d7") |
| conv2d | Ran | google-deepmind/deepmind-research/alphafold_casp13/two_dim_convnet.py code served (permissive licence) · get_code("27c6606148980218") |
| equal32 | Ran | google-deepmind/deepmind-research/galaxy_mergers/losses.py code served (permissive licence) · get_code("892496844acc7df6") |
| get_check_point_num | Ran | yuyudeep/hcmt/util.py pointer only (licence: NONE) · get_code("a951207c022a9c79") |
| get_mask_impact_normal | Ran | yuyudeep/hcmt/util.py pointer only (licence: NONE) · get_code("7182e4b73a148e4c") |
| mse_loss | Ran | google-deepmind/deepmind-research/galaxy_mergers/losses.py code served (permissive licence) · get_code("b914b7fa073cd865") |
| normalize_regression_loss | Ran | google-deepmind/deepmind-research/galaxy_mergers/losses.py code served (permissive licence) · get_code("f8ad297adc94df7d") |
| pairwise_dist | Ran | yuyudeep/hcmt/model.py pointer only (licence: NONE) · get_code("7872fb172aaec6b0") |
| relative_shift | Ran | google-deepmind/deepmind-research/enformer/attention_module.py code served (permissive licence) · get_code("cb4b6e4277150f01") |
| triangles_to_edges | Ran | yuyudeep/hcmt/util.py pointer only (licence: NONE) · get_code("3fc3d053754bcdfb") |
| bias_variable | Not yet run | google-deepmind/deepmind-research/alphafold_casp13/two_dim_convnet.py code served (permissive licence) · get_code("3bf8634c88f6bebc") |
| get_positional_feature_function | Not yet run | google-deepmind/deepmind-research/enformer/attention_module.py code served (permissive licence) · get_code("6ee8170a2950ea7c") |
| load_dataset | Not yet run | yuyudeep/hcmt/dataset.py pointer only (licence: NONE) · get_code("ace670d545ed0b9d") |
| positional_features_all | Not yet run | google-deepmind/deepmind-research/enformer/attention_module.py code served (permissive licence) · get_code("6a7819159276d4e5") |
| weight_variable | Not yet run | google-deepmind/deepmind-research/alphafold_casp13/two_dim_convnet.py code served (permissive licence) · get_code("7d7dfab4bbd647b3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recently, many mesh-based graph neural network (GNN) models have been proposed for modeling complex high-dimensional physical systems. Remarkable achievements have been made in significantly reducing the solving time compared to traditional numerical solvers. These methods are typically designed to i) reduce the computational cost in solving physical dynamics and/or ii) propose techniques to enhance the solution accuracy in fluid and rigid body dynamics. However, it remains under-explored whether they are effective in addressing the challenges of flexible body dynamics, where instantaneous collisions occur within a very short timeframe. In this paper, we present Hierarchical Contact Mesh Transformer (HCMT), which uses hierarchical mesh structures and can learn long-range dependencies (occurred by collisions) among spatially distant positions of a body -two close positions in a higher-level mesh correspond to two distant positions in a lower-level mesh. HCMT enables long-range interactions, and the hierarchical mesh structure quickly propagates collision effects to faraway positions. To this end, it consists of a contact mesh Transformer and a hierarchical mesh Transformer (CMT and HMT, respectively). Lastly, we propose a flexible body dynamics dataset, consisting of trajectories that reflect experimental settings frequently used in the display industry for product designs. We also compare the performance of several baselines using well-known benchmark datasets. Our results show that HCMT provides significant performance improvements over existing methods. Our code is available at https://github.com/yuyudeep/hcmt.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.12467")
get_code_for_paper("2312.12467")
have("2312.12467")
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