We lifted 13 functions out of this paper's own repositories and ran 11 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 |
|---|---|---|
| dimitra-maoutsa/odes_for_sdes | pwc_unofficial | 11 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| RGB_to_hex | Ran | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/experiments/colors2.py code served (permissive licence) · get_code("fd775635d6cd6f7a") |
| color_dict | Ran | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/experiments/colors2.py code served (permissive licence) · get_code("e87f6299b349de4e") |
| f | Ran | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/models/FHN_neuron.py code served (permissive licence) · get_code("ee4a6b293a38def0") |
| f | Ran | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/models/two_dim_multiwell.py code served (permissive licence) · get_code("5dc93676e0febd49") |
| hex_to_RGB | Ran | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/experiments/colors2.py code served (permissive licence) · get_code("4c4733cf626a00f0") |
| low_rank_approx | Ran | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/experiments/2D_OU/score_function_sparse.py code served (permissive licence) · get_code("6963771d5cd0a059") |
| my_cdist | Ran | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/score_estimators/score_function_estimators.py code served (permissive licence) · get_code("c65bfe92af67d2ce") |
| score_function | Ran | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/experiments/2D_OU/score_fucntion2.py code served (permissive licence) · get_code("bf7420d1d5ca19f5") |
| score_function_multid_seperate | Ran | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/score_estimators/score_function_estimators.py code served (permissive licence) · get_code("62e82dd6dedd6b5b") |
| score_function_multid_seperate | Ran | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/experiments/2D_OU/score_function_multid_seperate.py code served (permissive licence) · get_code("9f6e122852349c77") |
| score_function_multid_seperate_all_dims | Ran | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/score_estimators/score_function_estimators.py code served (permissive licence) · get_code("4ba4fdc86131d50d") |
| score_function_sparse | Not yet run | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/experiments/2D_OU/score_function_sparse.py code served (permissive licence) · get_code("dc7fb4998159aabd") |
| torched_score_function_multid_seperate_all_dims | Not yet run | dimitra-maoutsa/odes_for_sdes/odes_for_sdes/score_estimators/score_function_estimators_pytorch.py code served (permissive licence) · get_code("acfd9b0dd314d52a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Fokker-Planck equations are extensively employed in various scientific fields as they characterise the behaviour of stochastic systems at the level of probability density functions. Although broadly used, they allow for analytical treatment only in limited settings, and often is inevitable to resort to numerical solutions. Here, we develop a computational approach for simulating the time evolution of Fokker-Planck solutions in terms of a mean field limit of an interacting particle system. The interactions between particles are determined by the gradient of the logarithm of the particle density, approximated here by a novel statistical estimator. The performance of our method shows promising results, with more accurate and less fluctuating statistics compared to direct stochastic simulations of comparable particle number. Taken together, our framework allows for effortless and reliable particle-based simulations of Fokker-Planck equations in low and moderate dimensions. The proposed gradient-log-density estimator is also of independent interest, for example, in the context of optimal control.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2006.00702")
get_code_for_paper("2006.00702")
have("2006.00702")
Connect an agent — have() is free.