We lifted 8 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| rushilanirudh/macc | canonical | 3 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| load_dataset | Ran | rushilanirudh/macc/wae_metric/run_WAE.py code served (permissive licence) · get_code("d0f29d9b389ba68d") |
| sample_eps | Ran | rushilanirudh/macc/utils.py code served (permissive licence) · get_code("84887f40fce49bb3") |
| sample_z | Ran | rushilanirudh/macc/wae_metric/run_WAE.py code served (permissive licence) · get_code("67cb92b0dd5f53cf") |
| gen_encoder_FCN | Not yet run | rushilanirudh/macc/wae_metric/model_AVB.py code served (permissive licence) · get_code("c82291ccb06b17eb") |
| log | Not yet run | rushilanirudh/macc/utils.py code served (permissive licence) · get_code("f7d44a32801617e6") |
| var_decoder_FCN | Not yet run | rushilanirudh/macc/wae_metric/model_AVB.py code served (permissive licence) · get_code("72e86d3cbb9a28b0") |
| xavier_init | Not yet run | rushilanirudh/macc/utils.py code served (permissive licence) · get_code("139e368dce2e10a1") |
| xavier_init | Not yet run | rushilanirudh/macc/wae_metric/model_AVB.py code served (permissive licence) · get_code("5eb1ccd14f3b5a80") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Neural networks have become very popular in surrogate modeling because of their ability to characterize arbitrary, high dimensional functions in a data driven fashion. This paper advocates for the training of surrogates that are consistent with the physical manifold -- i.e., predictions are always physically meaningful, and are cyclically consistent -- i.e., when the predictions of the surrogate, when passed through an independently trained inverse model give back the original input parameters. We find that these two consistencies lead to surrogates that are superior in terms of predictive performance, more resilient to sampling artifacts, and tend to be more data efficient. Using Inertial Confinement Fusion (ICF) as a test bed problem, we model a 1D semi-analytic numerical simulator and demonstrate the effectiveness of our approach. Code and data are available at https://github.com/rushilanirudh/macc/
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1912.08113")
get_code_for_paper("1912.08113")
have("1912.08113")
Connect an agent — have() is free.