{"total":144,"rows":[{"id":"2404.19756","title":"KAN: Kolmogorov-Arnold Networks","created":"2024-04-30","cited_by_count":649,"area":null},{"id":"2106.06427","title":"Neural Symbolic Regression that Scales","created":"2021-06-11","cited_by_count":192,"area":null},{"id":"2404.18400","title":"LLM-SR: Scientific Equation Discovery via Programming with Large Language Models","created":"2024-04-29","cited_by_count":133,"area":null},{"id":"2408.10205","title":"KAN 2.0: Kolmogorov-Arnold Networks Meet Science","created":"2024-08-19","cited_by_count":118,"area":null},{"id":"2405.09783","title":"LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery","created":"2024-05-16","cited_by_count":86,"area":"lr"},{"id":"2303.06833","title":"Transformer-based Planning for Symbolic Regression","created":"2023-03-13","cited_by_count":79,"area":null},{"id":"2312.14688","title":"A Mathematical Guide to Operator Learning","created":"2023-12-22","cited_by_count":76,"area":null},{"id":"2205.13134","title":"Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search","created":"2022-05-26","cited_by_count":66,"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":"2412.11427","title":"Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges","created":"2024-12-16","cited_by_count":41,"area":null},{"id":"2305.08746","title":"Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability","created":"2023-05-04","cited_by_count":38,"area":"mechinterp"},{"id":"2305.13341","title":"Discovering Causal Relations and Equations from Data","created":"2023-05-21","cited_by_count":36,"area":null},{"id":"2510.07172","title":"NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents","created":"2025-10-08","cited_by_count":36,"area":"agents"},{"id":"2109.09721","title":"Machine-learning hidden symmetries","created":"2021-09-20","cited_by_count":31,"area":null},{"id":"2209.06257","title":"A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge","created":"2022-09-13","cited_by_count":31,"area":null},{"id":"2211.11461","title":"Exhaustive Symbolic Regression","created":"2022-11-21","cited_by_count":29,"area":null},{"id":"2204.12789","title":"Learning Green's functions associated with time-dependent partial differential equations","created":"2022-04-27","cited_by_count":26,"area":null},{"id":"2307.12617","title":"Predicting Ordinary Differential Equations with Transformers","created":"2023-07-24","cited_by_count":26,"area":null},{"id":"2311.04128","title":"Generative learning for nonlinear dynamics","created":"2023-11-07","cited_by_count":22,"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":"2304.10336","title":"Controllable Neural Symbolic Regression","created":"2023-04-20","cited_by_count":20,"area":null},{"id":"2402.05110","title":"Opening the AI black box: program synthesis via mechanistic interpretability","created":"2024-02-07","cited_by_count":20,"area":"mechinterp"},{"id":"2205.09751","title":"Taylor Genetic Programming for Symbolic Regression","created":"2022-04-28","cited_by_count":19,"area":null},{"id":"2302.10787","title":"Benchmarking sparse system identification with low-dimensional chaos","created":"2023-02-04","cited_by_count":19,"area":null},{"id":"2305.02251","title":"Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems","created":"2023-05-03","cited_by_count":19,"area":null},{"id":"2308.09474","title":"Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbert","created":"2023-08-18","cited_by_count":18,"area":null},{"id":"2411.03753","title":"Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length","created":"2024-11-06","cited_by_count":17,"area":null},{"id":"2309.13705","title":"A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data","created":"2023-09-24","cited_by_count":16,"area":null},{"id":"2602.12259","title":"Think like a Scientist: Physics-guided LLM Agent for Equation Discovery","created":"2026-02-12","cited_by_count":16,"area":"lr"},{"id":"2210.13447","title":"Precision Machine Learning","created":"2022-10-24","cited_by_count":14,"area":null},{"id":"2503.23616","title":"Interpretable Machine Learning in Physics: A Review","created":"2025-03-30","cited_by_count":14,"area":null},{"id":"2511.07262","title":"AgenticSciML: Collaborative Multi-Agent Systems for Emergent Discovery in Scientific Machine Learning","created":"2025-11-10","cited_by_count":14,"area":null},{"id":"2111.12210","title":"From Kepler to Newton: Explainable AI for Science","created":"2021-11-24","cited_by_count":13,"area":null},{"id":"2402.18603","title":"MMSR: Symbolic Regression is a Multi-Modal Information Fusion Task","created":"2024-02-28","cited_by_count":13,"area":null},{"id":"2109.13901","title":"Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning","created":"2021-09-28","cited_by_count":11,"area":null},{"id":"2205.15569","title":"GSR: A Generalized Symbolic Regression Approach","created":"2022-05-31","cited_by_count":11,"area":null},{"id":"2310.05227","title":"Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology","created":"2023-10-08","cited_by_count":11,"area":null},{"id":"2503.07994","title":"A Neural Symbolic Model for Space Physics","created":"2025-03-11","cited_by_count":11,"area":null},{"id":"2110.08058","title":"Quantifying Local Specialization in Deep Neural Networks","created":"2021-10-13","cited_by_count":10,"area":null},{"id":"2209.11144","title":"Automatic and effective discovery of quantum kernels","created":"2022-09-22","cited_by_count":10,"area":null},{"id":"2304.06333","title":"Priors for symbolic regression","created":"2023-04-13","cited_by_count":10,"area":null},{"id":"2401.14424","title":"Discovering Mathematical Formulas from Data via GPT-guided Monte Carlo Tree Search","created":"2024-01-24","cited_by_count":10,"area":null},{"id":"2509.14026","title":"Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks","created":"2025-09-17","cited_by_count":10,"area":null},{"id":"2602.20557","title":"GENSR: Symbolic Regression Based in Equation Generative Space","created":"2026-02-24","cited_by_count":9,"area":null},{"id":"2109.01634","title":"AI Descartes: Combining Data and Theory for Derivable Scientific Discovery","created":"2021-09-03","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":"2107.07345","title":"Inferring the Structure of Ordinary Differential Equations","created":"2021-07-05","cited_by_count":7,"area":null},{"id":"2206.10586","title":"D-CIPHER: Discovery of Closed-form Partial Differential Equations","created":"2022-06-21","cited_by_count":7,"area":null},{"id":"2303.07009","title":"Symbolic Regression for PDEs using Pruned Differentiable Programs","created":"2023-03-13","cited_by_count":7,"area":null},{"id":"2312.10913","title":"GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations","created":"2023-12-18","cited_by_count":7,"area":null},{"id":"2410.17218","title":"Creativity in AI: Progresses and Challenges","created":"2024-10-22","cited_by_count":7,"area":null},{"id":"2503.09620","title":"Exploiting Edited Large Language Models as General Scientific Optimizers","created":"2025-03-08","cited_by_count":7,"area":"lr"},{"id":"2509.01398","title":"The Need for Verification in AI-Driven Scientific Discovery","created":"2025-09-01","cited_by_count":7,"area":null},{"id":"2304.01117","title":"Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition","created":"2023-04-03","cited_by_count":6,"area":null},{"id":"2306.04120","title":"MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation","created":"2023-06-07","cited_by_count":6,"area":null},{"id":"2312.00396","title":"GFN-SR: Symbolic Regression with Generative Flow Networks","created":"2023-12-01","cited_by_count":6,"area":null},{"id":"2501.09283","title":"Free-Knots Kolmogorov-Arnold Network: On the Analysis of Spline Knots and Advancing Stability","created":"2025-01-16","cited_by_count":6,"area":null},{"id":"2505.12083","title":"Discovering Symbolic Differential Equations with Symmetry Invariants","created":"2025-05-17","cited_by_count":6,"area":null},{"id":"2401.15103","title":"PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression","created":"2024-01-25","cited_by_count":5,"area":null},{"id":"2403.01723","title":"Statistical Mechanics of Dynamical System Identification","created":"2024-03-04","cited_by_count":5,"area":null},{"id":"2406.03585","title":"A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming","created":"2024-06-05","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":"2103.13496","title":"Similarity-Based Equational Inference in Physics","created":"2021-03-24","cited_by_count":4,"area":null},{"id":"2209.10861","title":"A novel corrective-source term approach to modeling unknown physics in aluminum extraction process","created":"2022-09-22","cited_by_count":4,"area":null},{"id":"2301.06064","title":"Symbolic expression generation via Variational Auto-Encoder","created":"2023-01-15","cited_by_count":4,"area":null},{"id":"2505.15572","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","created":"2025-05-21","cited_by_count":4,"area":null},{"id":"2506.03302","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","created":"2025-06-03","cited_by_count":4,"area":null},{"id":"2506.15881","title":"T-SHRED: Symbolic Regression for Regularization and Model Discovery with Transformer Shallow Recurrent Decoders","created":"2025-06-18","cited_by_count":4,"area":null},{"id":"2102.08351","title":"Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics","created":"2021-02-16","cited_by_count":3,"area":null},{"id":"2203.08808","title":"Neural-Network-Directed Genetic Programmer for Discovery of Governing Equations","created":"2022-03-15","cited_by_count":3,"area":null},{"id":"2207.06240","title":"Physics Informed Symbolic Networks","created":"2022-07-11","cited_by_count":3,"area":null},{"id":"2209.04726","title":"Data-driven, multi-moment fluid modeling of Landau damping","created":"2022-09-10","cited_by_count":3,"area":null},{"id":"2211.02830","title":"Discovering ordinary differential equations that govern time-series","created":"2022-11-05","cited_by_count":3,"area":null},{"id":"2306.04718","title":"Scalable Neural Symbolic Regression using Control Variables","created":"2023-06-07","cited_by_count":3,"area":null},{"id":"2311.12579","title":"Machine-Guided Discovery of a Real-World Rogue Wave Model","created":"2023-11-21","cited_by_count":3,"area":null},{"id":"2404.05908","title":"Interpretability in Symbolic Regression: a benchmark of Explanatory Methods using the Feynman data set","created":"2024-04-08","cited_by_count":3,"area":null},{"id":"2410.01752","title":"TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery","created":"2024-10-02","cited_by_count":3,"area":null},{"id":"2410.13681","title":"Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$","created":"2024-10-17","cited_by_count":3,"area":null},{"id":"2505.10762","title":"Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics","created":"2025-05-16","cited_by_count":3,"area":"deeprl"},{"id":"2510.08317","title":"Iterated Agent for Symbolic Regression","created":"2025-10-09","cited_by_count":3,"area":"agents"},{"id":"2511.08570","title":"Automatic Grid Updates for Kolmogorov-Arnold Networks using Layer Histograms","created":"2025-11-11","cited_by_count":3,"area":null},{"id":"2109.07359","title":"Modular Neural Ordinary Differential Equations","created":"2021-09-15","cited_by_count":2,"area":null},{"id":"2109.10303","title":"Computing Complexity-aware Plans Using Kolmogorov Complexity","created":"2021-09-21","cited_by_count":2,"area":null},{"id":"2305.00557","title":"Collective Relational Inference for learning heterogeneous interactions","created":"2023-04-30","cited_by_count":2,"area":null},{"id":"2404.15742","title":"Generalizing the SINDy approach with nested neural networks","created":"2024-04-24","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":"2412.11139","title":"ViSymRe: Vision Multimodal Symbolic Regression","created":"2024-12-15","cited_by_count":2,"area":null},{"id":"2501.18563","title":"No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs","created":"2025-01-30","cited_by_count":2,"area":null},{"id":"2508.21484","title":"Data-driven Discovery of Digital Twins in Biomedical Research","created":"2025-08-29","cited_by_count":2,"area":null},{"id":"2511.05849","title":"EGG-SR: Embedding Symbolic Equivalence into Symbolic Regression via Equality Graph","created":"2025-11-08","cited_by_count":2,"area":null},{"id":"2511.09906","title":"Beyond empirical models: Discovering new constitutive laws in solids with graph-based equation discovery","created":"2025-11-13","cited_by_count":2,"area":null},{"id":"2601.05770","title":"Weights to Code: Extracting Interpretable Algorithms from the Discrete Transformer","created":"2026-01-09","cited_by_count":2,"area":null},{"id":"2103.13939","title":"Recovering Quantitative Models of Human Information Processing with Differentiable Architecture Search","created":"2021-03-25","cited_by_count":1,"area":null},{"id":"2105.14396","title":"SyReNets: Symbolic Residual Neural Networks","created":"2021-05-30","cited_by_count":1,"area":null},{"id":"2306.00153","title":"Information Fusion via Symbolic Regression: A Tutorial in the Context of Human Health","created":"2023-05-31","cited_by_count":1,"area":null},{"id":"2306.08506","title":"Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning","created":"2023-06-14","cited_by_count":1,"area":"lr"},{"id":"2311.11847","title":"Deep learning complete intersection Calabi-Yau manifolds","created":"2023-11-20","cited_by_count":1,"area":null},{"id":"2311.15865","title":"A precise symbolic emulator of the linear matter power spectrum","created":"2023-11-27","cited_by_count":1,"area":null}],"next_cursor":"100"}