{"list":"other","total":65,"rows":[{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"2010.08895","title":"Fourier Neural Operator for Parametric Partial Differential Equations","created":"2020-10-18","cited_by_count":2908},{"id":"2202.11214","title":"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators","created":"2022-02-22","cited_by_count":776},{"id":"1906.01563","title":"Hamiltonian Neural Networks","created":"2019-06-04","cited_by_count":712},{"id":"2101.03164","title":"E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials","created":"2021-01-08","cited_by_count":708},{"id":"2201.05624","title":"Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next","created":"2022-01-14","cited_by_count":608},{"id":"1905.11481","title":"AI Feynman: a Physics-Inspired Method for Symbolic Regression","created":"2019-05-27","cited_by_count":548},{"id":"2102.01010","title":"Machine learning accelerated computational fluid dynamics","created":"2021-01-28","cited_by_count":471},{"id":"2210.07182","title":"PDEBENCH: An Extensive Benchmark for Scientific Machine Learning","created":"2022-10-13","cited_by_count":413},{"id":"2003.04630","title":"Lagrangian Neural Networks","created":"2020-03-10","cited_by_count":398},{"id":"1912.04871","title":"Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients","created":"2019-12-10","cited_by_count":309},{"id":"1801.06637","title":"Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations","created":"2018-01-20","cited_by_count":235},{"id":"2309.15325","title":"Neural Operators for Accelerating Scientific Simulations and Design","created":"2023-09-27","cited_by_count":221},{"id":"2107.14351","title":"Contemporary Symbolic Regression Methods and their Relative Performance","created":"2021-07-29","cited_by_count":215},{"id":"2204.10532","title":"End-to-end symbolic regression with transformers","created":"2022-04-22","cited_by_count":203},{"id":"2405.19101","title":"Poseidon: Efficient Foundation Models for PDEs","created":"2024-05-29","cited_by_count":200},{"id":"2106.06427","title":"Neural Symbolic Regression that Scales","created":"2021-06-11","cited_by_count":192},{"id":"2208.05419","title":"FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators","created":"2022-08-08","cited_by_count":188},{"id":"2312.15796","title":"GenCast: Diffusion-based ensemble forecasting for medium-range weather","created":"2023-12-25","cited_by_count":177},{"id":"2005.03448","title":"Physics-informed learning of governing equations from scarce data","created":"2020-05-05","cited_by_count":167},{"id":"1905.08883","title":"Explainable Machine Learning for Scientific Insights and Discoveries","created":"2019-05-21","cited_by_count":166},{"id":"2211.02556","title":"Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast","created":"2022-11-03","cited_by_count":166},{"id":"1806.07259","title":"Learning Equations for Extrapolation and Control","created":"2018-06-19","cited_by_count":156},{"id":"2412.00568","title":"The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning","created":"2024-11-30","cited_by_count":145},{"id":"2110.02085","title":"Enhancing Computational Fluid Dynamics with Machine Learning","created":"2021-10-05","cited_by_count":138},{"id":"2404.18400","title":"LLM-SR: Scientific Equation Discovery via Programming with Large Language Models","created":"2024-04-29","cited_by_count":133},{"id":"2004.02322","title":"SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics","created":"2020-04-05","cited_by_count":125},{"id":"1610.02995","title":"Extrapolation and learning equations","created":"2016-10-10","cited_by_count":110},{"id":"2110.13040","title":"Neural Flows: Efficient Alternative to Neural ODEs","created":"2021-10-25","cited_by_count":101},{"id":"2407.07218","title":"Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations","created":"2024-07-09","cited_by_count":91},{"id":"2409.09359","title":"Symbolic Regression with a Learned Concept Library","created":"2024-09-14","cited_by_count":83},{"id":"1904.09406","title":"DeepMoD: Deep learning for Model Discovery in noisy data","created":"2019-04-20","cited_by_count":71},{"id":"2302.04107","title":"Can Physics-Informed Neural Networks beat the Finite Element Method?","created":"2023-02-08","cited_by_count":68},{"id":"2205.13134","title":"Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search","created":"2022-05-26","cited_by_count":66},{"id":"2305.19913","title":"Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning","created":"2023-05-31","cited_by_count":57},{"id":"1909.05862","title":"Learning Symbolic Physics with Graph Networks","created":"2019-09-12","cited_by_count":53},{"id":"2410.23179","title":"Does equivariance matter at scale?","created":"2024-10-30","cited_by_count":52},{"id":"1707.08528","title":"Extracting Sparse High-Dimensional Dynamics from Limited Data","created":"2017-07-26","cited_by_count":50},{"id":"2112.03769","title":"Machine Learning in the Search for New Fundamental Physics","created":"2021-12-07","cited_by_count":50},{"id":"2210.17051","title":"Real-time high-resolution CO$_2$ geological storage prediction using nested Fourier neural operators","created":"2022-10-31","cited_by_count":47},{"id":"2507.06952","title":"What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models","created":"2025-07-09","cited_by_count":47},{"id":"2003.11755","title":"A Survey of Deep Learning for Scientific Discovery","created":"2020-03-26","cited_by_count":36},{"id":"2311.05967","title":"Plasma Surrogate Modelling using Fourier Neural Operators","created":"2023-11-10","cited_by_count":29},{"id":"2401.00282","title":"Deep Generative Symbolic Regression","created":"2023-12-30","cited_by_count":28},{"id":"2410.02698","title":"Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups","created":"2024-10-03","cited_by_count":24},{"id":"1809.09170","title":"Numerical Aspects for Approximating Governing Equations Using Data","created":"2018-09-24","cited_by_count":21},{"id":"2212.04971","title":"PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data","created":"2022-12-09","cited_by_count":20},{"id":"2302.10787","title":"Benchmarking sparse system identification with low-dimensional chaos","created":"2023-02-04","cited_by_count":19},{"id":"2209.12340","title":"Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator","created":"2022-09-25","cited_by_count":18},{"id":"2210.10663","title":"A Review of Data-Driven Discovery for Dynamic Systems","created":"2022-10-19","cited_by_count":18},{"id":"2308.09474","title":"Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbert","created":"2023-08-18","cited_by_count":18},{"id":"2510.23166","title":"Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms","created":"2025-10-27","cited_by_count":13},{"id":"2301.08290","title":"Forecasting subcritical cylinder wakes with Fourier Neural Operators","created":"2023-01-19","cited_by_count":11},{"id":"2103.05660","title":"Identifiability Analysis of Linear Ordinary Differential Equation Systems with a Single Trajectory","created":"2021-03-09","cited_by_count":10},{"id":"2310.05537","title":"ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization","created":"2023-10-09","cited_by_count":10},{"id":"1910.10262","title":"Learning Partial Differential Equations from Data Using Neural Networks","created":"2019-10-22","cited_by_count":8},{"id":"2210.08342","title":"Symbolic Recovery of Differential Equations: The Identifiability Problem","created":"2022-10-15","cited_by_count":8},{"id":"2202.10915","title":"Learning-informed parameter identification in nonlinear time-dependent PDEs","created":"2022-02-22","cited_by_count":7},{"id":"2206.10586","title":"D-CIPHER: Discovery of Closed-form Partial Differential Equations","created":"2022-06-21","cited_by_count":7},{"id":"2506.09816","title":"Identifiability Challenges in Sparse Linear Ordinary Differential Equations","created":"2025-06-11","cited_by_count":7},{"id":"2410.22009","title":"On uniqueness in structured model learning","created":"2024-10-29","cited_by_count":4},{"id":"2410.09938","title":"Robust identifiability for symbolic recovery of differential equations","created":"2024-10-13","cited_by_count":3},{"id":"2502.20603","title":"Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems","created":"2025-02-28","cited_by_count":3},{"id":"2205.06231","title":"The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control","created":"2022-05-12","cited_by_count":2},{"id":"2507.11535","title":"Canonical Bayesian Linear System Identification","created":"2025-07-15","cited_by_count":1}],"next_cursor":null}