{"total":71,"rows":[{"id":"2408.10205","title":"KAN 2.0: Kolmogorov-Arnold Networks Meet Science","created":"2024-08-19","cited_by_count":118,"area":null},{"id":"2305.01582","title":"Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl","created":"2023-05-02","cited_by_count":54,"area":null},{"id":"2206.10540","title":"Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery","created":"2022-06-21","cited_by_count":46,"area":null},{"id":"2112.03321","title":"Noether Networks: Meta-Learning Useful Conserved Quantities","created":"2021-12-06","cited_by_count":26,"area":null},{"id":"2301.05638","title":"Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles","created":"2023-01-13","cited_by_count":22,"area":null},{"id":"2104.05417","title":"An Approach to Symbolic Regression Using Feyn","created":"2021-04-12","cited_by_count":20,"area":null},{"id":"2203.12610","title":"AI Poincar\\'{e} 2.0: Machine Learning Conservation Laws from Differential Equations","created":"2022-03-23","cited_by_count":20,"area":null},{"id":"2503.23616","title":"Interpretable Machine Learning in Physics: A Review","created":"2025-03-30","cited_by_count":14,"area":null},{"id":"2103.15147","title":"Symbolic regression outperforms other models for small data sets","created":"2021-03-28","cited_by_count":12,"area":null},{"id":"2208.14995","title":"Discovering Conservation Laws using Optimal Transport and Manifold Learning","created":"2022-08-31","cited_by_count":12,"area":null},{"id":"2210.00272","title":"FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities","created":"2022-10-01","cited_by_count":12,"area":null},{"id":"2106.00026","title":"Machine-Learning Non-Conservative Dynamics for New-Physics Detection","created":"2021-05-31","cited_by_count":10,"area":null},{"id":"2106.13301","title":"Physics perception in sloshing scenes with guaranteed thermodynamic consistency","created":"2021-06-24","cited_by_count":10,"area":null},{"id":"2301.00957","title":"Metalearning generalizable dynamics from trajectories","created":"2023-01-03","cited_by_count":9,"area":null},{"id":"2306.08057","title":"Symbolic Regression via Control Variable Genetic Programming","created":"2023-05-25","cited_by_count":9,"area":null},{"id":"2102.04008","title":"Discovering conservation laws from trajectories via machine learning","created":"2021-02-08","cited_by_count":8,"area":null},{"id":"2302.05383","title":"Discovering Sparse Representations of Lie Groups with Machine Learning","created":"2023-02-10","cited_by_count":8,"area":null},{"id":"2305.14656","title":"RSRM: Reinforcement Symbolic Regression Machine","created":"2023-05-24","cited_by_count":8,"area":null},{"id":"2406.02154","title":"Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation law","created":"2024-06-04","cited_by_count":7,"area":null},{"id":"2201.01305","title":"Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter","created":"2022-01-04","cited_by_count":6,"area":null},{"id":"2304.12865","title":"Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks","created":"2023-04-24","cited_by_count":6,"area":null},{"id":"2307.04891","title":"Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6","created":"2023-07-10","cited_by_count":6,"area":null},{"id":"2309.07860","title":"Identifying the Group-Theoretic Structure of Machine-Learned Symmetries","created":"2023-09-14","cited_by_count":6,"area":null},{"id":"2302.00806","title":"Oracle-Preserving Latent Flows","created":"2023-02-02","cited_by_count":5,"area":null},{"id":"2302.05783","title":"ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction","created":"2023-02-11","cited_by_count":5,"area":null},{"id":"2504.01538","title":"AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge","created":"2025-04-02","cited_by_count":5,"area":null},{"id":"2506.19626","title":"Scaling Up Unbiased Search-based Symbolic Regression","created":"2025-06-24","cited_by_count":5,"area":null},{"id":"2111.13786","title":"Learning from learning machines: a new generation of AI technology to meet the needs of science","created":"2021-11-27","cited_by_count":4,"area":null},{"id":"2304.07247","title":"The R-mAtrIx Net","created":"2023-04-14","cited_by_count":4,"area":null},{"id":"2405.17209","title":"How Do Transformers \"Do\" Physics? Investigating the Simple Harmonic Oscillator","created":"2024-05-23","cited_by_count":4,"area":null},{"id":"2204.13376","title":"Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems","created":"2022-04-28","cited_by_count":3,"area":null},{"id":"2212.01168","title":"Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning","created":"2022-12-02","cited_by_count":3,"area":null},{"id":"2401.04978","title":"Closed-Form Interpretation of Neural Network Classifiers with Symbolic Gradients","created":"2024-01-10","cited_by_count":3,"area":null},{"id":"2409.01416","title":"Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching","created":"2024-09-02","cited_by_count":3,"area":null},{"id":"2410.05445","title":"Data-Driven Discovery of Conservation Laws from Trajectories via Neural Deflation","created":"2024-10-07","cited_by_count":3,"area":null},{"id":"2510.24160","title":"Identifiable learning of dissipative dynamics","created":"2025-10-28","cited_by_count":3,"area":null},{"id":"2307.05299","title":"Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks","created":"2023-07-11","cited_by_count":2,"area":null},{"id":"2405.06848","title":"ISR: Invertible Symbolic Regression","created":"2024-05-10","cited_by_count":2,"area":null},{"id":"2409.03986","title":"An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis","created":"2024-09-06","cited_by_count":2,"area":null},{"id":"2503.03014","title":"Learning finite symmetry groups of dynamical systems via equivariance detection","created":"2025-03-04","cited_by_count":2,"area":null},{"id":"2511.00102","title":"Automated Discovery of Conservation Laws via Hybrid Neural ODE-Transformers","created":"2025-10-30","cited_by_count":2,"area":null},{"id":"2512.02283","title":"Model Recovery at the Edge under Resource Constraints for Physical AI","created":"2025-12-01","cited_by_count":2,"area":null},{"id":"2209.01521","title":"Symplectically Integrated Symbolic Regression of Hamiltonian Dynamical Systems","created":"2022-09-04","cited_by_count":1,"area":null},{"id":"2210.07374","title":"A Relational Macrostate Theory Guides Artificial Intelligence to Learn Macro and Design Micro","created":"2022-10-13","cited_by_count":1,"area":null},{"id":"2301.07503","title":"Model-agnostic machine learning of conservation laws from data","created":"2023-01-12","cited_by_count":1,"area":null},{"id":"2305.06920","title":"Pseudo-Hamiltonian system identification","created":"2023-05-09","cited_by_count":1,"area":null},{"id":"2305.19525","title":"Discovering New Interpretable Conservation Laws as Sparse Invariants","created":"2023-05-31","cited_by_count":1,"area":null},{"id":"2312.11955","title":"Vertical Symbolic Regression","created":"2023-12-19","cited_by_count":1,"area":null},{"id":"2409.05305","title":"Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients","created":"2024-09-09","cited_by_count":1,"area":null},{"id":"2411.02058","title":"Intrinsic Dimensionality of Fermi-Pasta-Ulam-Tsingou High-Dimensional Trajectories Through Manifold Learning: A Linear Approach","created":"2024-11-04","cited_by_count":1,"area":null},{"id":"2412.12161","title":"Discover physical concepts and equations with machine learning","created":"2024-12-11","cited_by_count":1,"area":null},{"id":"2506.05245","title":"Robust Moment Identification for Nonlinear PDEs via a Neural ODE Approach","created":"2025-06-05","cited_by_count":1,"area":null},{"id":"2506.19550","title":"Discovering Symmetries of ODEs by Symbolic Regression","created":"2025-06-24","cited_by_count":1,"area":null},{"id":"2511.04564","title":"Uncertainties in Physics-informed Inverse Problems: The Hidden Risk in Scientific AI","created":"2025-11-06","cited_by_count":1,"area":null},{"id":"2604.17470","title":"Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data","created":"2026-04-19","cited_by_count":1,"area":null},{"id":"2210.02184","title":"Rediscovery of Numerical L\\\"uscher's Formula from the Neural Network","created":"2022-10-05","cited_by_count":0,"area":null},{"id":"2301.02432","title":"Myths and Legends in High-Performance Computing","created":"2023-01-06","cited_by_count":0,"area":null},{"id":"2301.09734","title":"Topological Learning in Multi-Class Data Sets","created":"2023-01-23","cited_by_count":0,"area":null},{"id":"2304.06522","title":"Dynamics-Based Intrinsic Signal Model for High-Dimensional, Small-Sample Data","created":"2023-04-13","cited_by_count":0,"area":null},{"id":"2307.06640","title":"Data-Scarce Identification of Game Dynamics via Sum-of-Squares Optimization","created":"2023-07-13","cited_by_count":0,"area":null},{"id":"2309.07344","title":"Efficient Learning of PDEs via Taylor Expansion and Sparse Decomposition into Value and Fourier Domains","created":"2023-09-13","cited_by_count":0,"area":null},{"id":"2310.10368","title":"Machine learning in physics: a short guide","created":"2023-10-16","cited_by_count":0,"area":null},{"id":"2312.10279","title":"A charge-preserving method for solving graph neural diffusion networks","created":"2023-12-16","cited_by_count":0,"area":null},{"id":"2405.02953","title":"Analysis of the Identifying Regulation with Adversarial Surrogates Algorithm","created":"2024-05-05","cited_by_count":0,"area":null},{"id":"2410.20951","title":"Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?","created":"2024-10-28","cited_by_count":0,"area":null},{"id":"2506.20799","title":"Structural System Identification via Validation and Adaptation","created":"2025-06-25","cited_by_count":0,"area":null},{"id":"2509.03769","title":"Deficiency of equation-finding approach to data-driven modeling of dynamical systems","created":"2025-09-03","cited_by_count":0,"area":null},{"id":"2603.20474","title":"From Data to Laws: Neural Discovery of Conservation Laws Without False Positives","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2604.23606","title":"Hamiltonian Graph Inference Networks: Joint structure discovery and dynamics prediction for lattice Hamiltonian systems from trajectory data","created":"2026-04-26","cited_by_count":0,"area":null},{"id":"2605.11524","title":"EqOD: Symmetry-Informed Stability Selection for PDE Identification","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2605.22330","title":"Symbolic Classification-Enabled LHC Limits for BSM Global Fits","created":"2026-05-21","cited_by_count":0,"area":null}],"next_cursor":null}