{"url":"/task/model-discovery","name":"Model Discovery","slug":"model-discovery","description_markdown":"discovering PDEs from spatiotemporal data","categories":[{"name":"Miscellaneous","url":"/area/miscellaneous"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":87,"papers_with_code":33,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":0,"subtasks":0,"parent_tasks":0},"benchmarks":[],"datasets":[],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":33,"tagged_in_all":87,"items":[{"url":"/paper/autotom-automated-bayesian-inverse-planning","title":"AutoToM: Automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of Mind","date":"2025-02-21","arxiv_id":"2502.15676","repositories_listed":2,"syntology":{"n":20,"n_ran":3,"n_unverified":17,"n_pointer_only":0}},{"url":"/paper/scalable-sparse-regression-for-model","title":"Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight","date":"2024-05-14","arxiv_id":"2405.09579","repositories_listed":2,"syntology":null},{"url":"/paper/auxiliary-functions-as-koopman-observables","title":"Auxiliary Functions as Koopman Observables: Data-Driven Analysis of Dynamical Systems via Polynomial Optimization","date":"2023-03-02","arxiv_id":"2303.01483","repositories_listed":2,"syntology":null},{"url":"/paper/a-new-family-of-constitutive-artificial","title":"A new family of Constitutive Artificial Neural Networks towards automated model discovery","date":"2022-09-15","arxiv_id":"2210.02202","repositories_listed":2,"syntology":null},{"url":"/paper/automatic-differentiation-to-simultaneously","title":"Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data","date":"2020-09-12","arxiv_id":"2009.08810","repositories_listed":2,"syntology":{"n":4,"n_ran":0,"n_unverified":4,"n_pointer_only":0}},{"url":"/paper/190409406","title":"DeepMoD: Deep learning for Model Discovery in noisy data","date":"2019-04-20","arxiv_id":"1904.09406","repositories_listed":2,"syntology":{"n":18,"n_ran":0,"n_unverified":18,"n_pointer_only":0}},{"url":"/paper/2506-08916","title":"Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning (ME-EQL)","date":"2025-06-10","arxiv_id":"2506.08916","repositories_listed":1,"syntology":null},{"url":"/paper/bi-level-optimization-for-parameter","title":"Bi-Level optimization for parameter estimation of differential equations using interpolation","date":"2025-05-31","arxiv_id":"2506.00720","repositories_listed":1,"syntology":null},{"url":"/paper/automated-modeling-method-for-pathloss-model","title":"Automated Modeling Method for Pathloss Model Discovery","date":"2025-05-29","arxiv_id":"2505.23383","repositories_listed":1,"syntology":null},{"url":"/paper/sodas-sparse-optimization-for-the-discovery","title":"SODAs: Sparse Optimization for the Discovery of Differential and Algebraic Equations","date":"2025-03-08","arxiv_id":"2503.05993","repositories_listed":1,"syntology":null},{"url":"/paper/symantic-an-efficient-symbolic-regression","title":"SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond","date":"2025-02-05","arxiv_id":"2502.03367","repositories_listed":1,"syntology":null},{"url":"/paper/boxinggym-benchmarking-progress-in-automated","title":"BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery","date":"2025-01-02","arxiv_id":"2501.01540","repositories_listed":1,"syntology":null},{"url":"/paper/torchsisso-a-pytorch-based-implementation-of","title":"TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery","date":"2024-10-02","arxiv_id":"2410.01752","repositories_listed":1,"syntology":null},{"url":"/paper/towards-model-discovery-using-domain","title":"Towards Model Discovery Using Domain Decomposition and PINNs","date":"2024-10-02","arxiv_id":"2410.01599","repositories_listed":1,"syntology":null},{"url":"/paper/hytas-a-hyperspectral-image-transformer","title":"HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis","date":"2024-07-23","arxiv_id":"2407.16269","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-uncertainty-guided-model-selection","title":"Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discovery","date":"2023-08-20","arxiv_id":"2308.10283","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-deep-learning-for-tumor-dynamic","title":"Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODE","date":"2023-08-02","arxiv_id":"2308.01362","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-hybrid-modeling-and-sorption-model","title":"Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach","date":"2023-03-22","arxiv_id":"2303.13555","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-scientific-discovery-with","title":"Interpretable Scientific Discovery with Symbolic Regression: A Review","date":"2022-11-20","arxiv_id":"2211.10873","repositories_listed":1,"syntology":null},{"url":"/paper/learning-sparse-nonlinear-dynamics-via-mixed","title":"Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimization","date":"2022-06-01","arxiv_id":"2206.00176","repositories_listed":1,"syntology":{"n":10,"n_ran":0,"n_unverified":10,"n_pointer_only":0}},{"url":"/paper/discrepancy-modeling-framework-learning","title":"Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects","date":"2022-03-10","arxiv_id":"2203.05164","repositories_listed":1,"syntology":null},{"url":"/paper/pysindy-a-comprehensive-python-package-for","title":"PySINDy: A comprehensive Python package for robust sparse system identification","date":"2021-11-12","arxiv_id":"2111.08481","repositories_listed":1,"syntology":null},{"url":"/paper/a-toolkit-for-data-driven-discovery-of","title":"A toolkit for data-driven discovery of governing equations in high-noise regimes","date":"2021-11-08","arxiv_id":"2111.04870","repositories_listed":1,"syntology":null},{"url":"/paper/discovering-pdes-from-multiple-experiments","title":"Discovering PDEs from Multiple Experiments","date":"2021-09-24","arxiv_id":"2109.11939","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_unverified":1,"n_pointer_only":4}},{"url":"/paper/sparsistent-model-discovery","title":"Sparsistent Model Discovery","date":"2021-06-22","arxiv_id":"2106.11936","repositories_listed":1,"syntology":null},{"url":"/paper/learning-normal-form-autoencoders-for-data","title":"Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations","date":"2021-06-09","arxiv_id":"2106.05102","repositories_listed":1,"syntology":null},{"url":"/paper/model-discovery-in-the-sparse-sampling-regime","title":"Model discovery in the sparse sampling regime","date":"2021-05-02","arxiv_id":"2105.00400","repositories_listed":1,"syntology":null},{"url":"/paper/gaussian-processes-meet-neuralodes-a-bayesian","title":"Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data","date":"2021-03-04","arxiv_id":"2103.03385","repositories_listed":1,"syntology":null},{"url":"/paper/sparsely-constrained-neural-networks-for","title":"Sparsely constrained neural networks for model discovery of PDEs","date":"2020-11-09","arxiv_id":"2011.04336","repositories_listed":1,"syntology":null},{"url":"/paper/learning-equations-from-biological-data-with","title":"Learning Equations from Biological Data with Limited Time Samples","date":"2020-05-19","arxiv_id":"2005.09622","repositories_listed":1,"syntology":null}],"syntology_records":5,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","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)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}