{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/lorenz","entry":"lorenz","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":11,"n_papers_ran":3,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":10,"n_samples_ran":2,"n_samples_fingerprinted":1,"n_places":12,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":8},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2605.02675","paper":"/paper/arxiv-2605-02675","title":"Online Generalised Predictive Coding","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"MLDawn/ODEM","path":"functions/generative_model/dynamics.py","file_url":"https://github.com/MLDawn/ODEM/blob/HEAD/functions/generative_model/dynamics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"d261852c71d3e0a7","mcp_get_code":{"code_sha256":"d261852c71d3e0a7"}},{"arxiv_id":"2502.09981","paper":"/paper/exploring-neural-granger-causality-with","title":"Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data","date":"2025-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"harpoonix/GC-xLSTM","path":"GC-xLSTM/synthetic.py","file_url":"https://github.com/harpoonix/GC-xLSTM/blob/HEAD/GC-xLSTM/synthetic.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be9afe4afc6f8b45","mcp_get_code":{"code_sha256":"be9afe4afc6f8b45"}},{"arxiv_id":"2411.06311","paper":"/paper/when-are-dynamical-systems-learned-from-time","title":"When are dynamical systems learned from time series data statistically accurate?","date":"2024-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ni-sha-c/stacNODE","path":"dyn_sys/dim3.py","file_url":"https://github.com/ni-sha-c/stacNODE/blob/HEAD/dyn_sys/dim3.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"93473ef0e934a7aa","mcp_get_code":{"code_sha256":"93473ef0e934a7aa"}},{"arxiv_id":"2409.08768","paper":"/paper/measure-theoretic-time-delay-embedding","title":"Measure-Theoretic Time-Delay Embedding","date":"2024-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jrbotvinick/Measure-Theoretic-Time-Delay-Embedding","path":"generate_data.py","file_url":"https://github.com/jrbotvinick/Measure-Theoretic-Time-Delay-Embedding/blob/HEAD/generate_data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3f6fbd888de2a3b0","mcp_get_code":{"code_sha256":"3f6fbd888de2a3b0"}},{"arxiv_id":"2108.13941","paper":"/paper/bubblewrap-online-tiling-and-real-time-flow","title":"Bubblewrap: Online tiling and real-time flow prediction on neural manifolds","date":"2021-08-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pearsonlab/bubblewrap","path":"datagen.py","file_url":"https://github.com/pearsonlab/bubblewrap/blob/HEAD/datagen.py","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"c78f70cc95349be0","mcp_get_code":{"code_sha256":"c78f70cc95349be0"}},{"arxiv_id":"2106.07688","paper":"/paper/next-generation-reservoir-computing","title":"Next Generation Reservoir Computing","date":"2021-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"quantinfo/ng-rc-paper-code","path":"LorenzConstLinQuadraticNVAR-Noise.py","file_url":"https://github.com/quantinfo/ng-rc-paper-code/blob/HEAD/LorenzConstLinQuadraticNVAR-Noise.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"54019732db0279c6","mcp_get_code":{"code_sha256":"54019732db0279c6"}},{"arxiv_id":"2106.07688","paper":"/paper/next-generation-reservoir-computing","title":"Next Generation Reservoir Computing","date":"2021-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"quantinfo/ng-rc-paper-code","path":"LorenzConstLinQuadraticNVARtimedelay-RK23.py","file_url":"https://github.com/quantinfo/ng-rc-paper-code/blob/HEAD/LorenzConstLinQuadraticNVARtimedelay-RK23.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8a5225640d787a1b","mcp_get_code":{"code_sha256":"8a5225640d787a1b"}},{"arxiv_id":"1907.04502","paper":"/paper/deepxde-a-deep-learning-library-for-solving","title":"DeepXDE: A deep learning library for solving differential equations","date":"2019-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"3d3bd15404080e16","mcp_get_code":{"code_sha256":"3d3bd15404080e16"}},{"arxiv_id":"1805.07411","paper":"/paper/discovery-of-nonlinear-multiscale-systems","title":"Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings","date":null,"month_inferred_from_arxiv_id":"2018-05","title_source":"archive","repo":"kpchamp/MultiscaleDiscovery","path":"utils/simulations.py","file_url":"https://github.com/kpchamp/MultiscaleDiscovery/blob/HEAD/utils/simulations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"61bb4253415897b6","mcp_get_code":{"code_sha256":"61bb4253415897b6"}},{"arxiv_id":"1803.07870","paper":"/paper/reservoir-computing-approaches-for","title":"Reservoir computing approaches for representation and classification of multivariate time series","date":"2018-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FilippoMB/Reservoir-Computing-framework-for-multivariate-time-series-classification","path":"reservoir_computing/datasets.py","file_url":"https://github.com/FilippoMB/Reservoir-Computing-framework-for-multivariate-time-series-classification/blob/HEAD/reservoir_computing/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f398b53bfd9d933f","mcp_get_code":{"code_sha256":"f398b53bfd9d933f"}},{"arxiv_id":"1711.10561","paper":"/paper/physics-informed-deep-learning-part-i-data","title":"Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations","date":"2017-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cianmscannell/pinns","path":"lorenz_main.py","file_url":"https://github.com/cianmscannell/pinns/blob/HEAD/lorenz_main.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3d3bd15404080e16","mcp_get_code":{"code_sha256":"3d3bd15404080e16"}},{"arxiv_id":"1303.4434","paper":"/paper/a-general-iterative-shrinkage-and","title":"A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems","date":"2013-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"icc2115/Neural-GC","path":"synthetic.py","file_url":"https://github.com/icc2115/Neural-GC/blob/HEAD/synthetic.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be9afe4afc6f8b45","mcp_get_code":{"code_sha256":"be9afe4afc6f8b45"}}]}