{"total":494,"rows":[{"id":"2102.01010","title":"Machine learning accelerated computational fluid dynamics","created":"2021-01-28","cited_by_count":471,"area":null},{"id":"2601.21998","title":"Causal World Modeling for Robot Control","created":"2026-01-29","cited_by_count":321,"area":"wm"},{"id":"2205.13671","title":"Transformer for Partial Differential Equations' Operator Learning","created":"2022-05-26","cited_by_count":283,"area":null},{"id":"2102.12086","title":"Modern Koopman Theory for Dynamical Systems","created":"2021-02-24","cited_by_count":276,"area":null},{"id":"2110.02085","title":"Enhancing Computational Fluid Dynamics with Machine Learning","created":"2021-10-05","cited_by_count":138,"area":null},{"id":"2305.18803","title":"Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors","created":"2023-05-30","cited_by_count":113,"area":null},{"id":"2310.02994","title":"Multiple Physics Pretraining for Physical Surrogate Models","created":"2023-10-04","cited_by_count":98,"area":null},{"id":"2112.15275","title":"Learned Coarse Models for Efficient Turbulence Simulation","created":"2021-12-31","cited_by_count":91,"area":null},{"id":"2110.05266","title":"Chaos as an interpretable benchmark for forecasting and data-driven modelling","created":"2021-10-11","cited_by_count":88,"area":null},{"id":"2201.09113","title":"Predicting Physics in Mesh-reduced Space with Temporal Attention","created":"2022-01-22","cited_by_count":84,"area":null},{"id":"2202.02306","title":"Rediscovering orbital mechanics with machine learning","created":"2022-02-04","cited_by_count":71,"area":null},{"id":"2309.01745","title":"Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation","created":"2023-09-04","cited_by_count":71,"area":"diffusion"},{"id":"2205.14027","title":"Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces","created":"2022-05-27","cited_by_count":67,"area":null},{"id":"2203.16797","title":"When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning","created":"2022-03-31","cited_by_count":65,"area":null},{"id":"2106.04781","title":"Encoding physics to learn reaction-diffusion processes","created":"2021-06-09","cited_by_count":61,"area":null},{"id":"2201.05136","title":"Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders","created":"2022-01-13","cited_by_count":58,"area":null},{"id":"2206.02607","title":"CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations","created":"2022-06-06","cited_by_count":58,"area":null},{"id":"2306.04406","title":"Generalized Teacher Forcing for Learning Chaotic Dynamics","created":"2023-06-07","cited_by_count":58,"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":"2203.15706","title":"Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems","created":"2022-03-29","cited_by_count":50,"area":null},{"id":"2310.02619","title":"Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs","created":"2023-10-04","cited_by_count":49,"area":null},{"id":"2409.20371","title":"Frequency Adaptive Normalization For Non-stationary Time Series Forecasting","created":"2024-09-30","cited_by_count":47,"area":null},{"id":"2108.05928","title":"Data-driven discovery of intrinsic dynamics","created":"2021-08-12","cited_by_count":45,"area":null},{"id":"2109.01514","title":"Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows","created":"2021-09-03","cited_by_count":45,"area":null},{"id":"2111.14889","title":"Rigorous data-driven computation of spectral properties of Koopman operators for dynamical systems","created":"2021-11-29","cited_by_count":45,"area":null},{"id":"2107.01272","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","created":"2021-07-02","cited_by_count":44,"area":null},{"id":"2402.00712","title":"ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction","created":"2024-02-01","cited_by_count":43,"area":null},{"id":"2111.04941","title":"Solving PDE-constrained Control Problems Using Operator Learning","created":"2021-11-09","cited_by_count":42,"area":null},{"id":"2202.06988","title":"Learned Turbulence Modelling with Differentiable Fluid Solvers: Physics-based Loss-functions and Optimisation Horizons","created":"2022-02-14","cited_by_count":39,"area":null},{"id":"2110.15013","title":"Deeptime: a Python library for machine learning dynamical models from time series data","created":"2021-10-28","cited_by_count":38,"area":null},{"id":"2304.14118","title":"Learning Neural PDE Solvers with Parameter-Guided Channel Attention","created":"2023-04-27","cited_by_count":38,"area":null},{"id":"2603.12231","title":"Temporal Straightening for Latent Planning","created":"2026-03-12","cited_by_count":38,"area":"wm"},{"id":"2204.03216","title":"Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data","created":"2022-04-07","cited_by_count":37,"area":null},{"id":"2210.03675","title":"Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts","created":"2022-10-07","cited_by_count":37,"area":null},{"id":"2301.12832","title":"Deep networks for system identification: a Survey","created":"2023-01-30","cited_by_count":37,"area":null},{"id":"2305.13341","title":"Discovering Causal Relations and Equations from Data","created":"2023-05-21","cited_by_count":36,"area":null},{"id":"2607.08639","title":"Native Video-Action Pretraining for Generalizable Robot Control","created":"2026-07-09","cited_by_count":36,"area":null},{"id":"2402.18377","title":"Out-of-Domain Generalization in Dynamical Systems Reconstruction","created":"2024-02-28","cited_by_count":33,"area":null},{"id":"2407.03475","title":"How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks","created":"2024-07-03","cited_by_count":31,"area":"distill"},{"id":"2109.00060","title":"Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations","created":"2021-08-31","cited_by_count":30,"area":null},{"id":"2206.00176","title":"Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimization","created":"2022-06-01","cited_by_count":29,"area":null},{"id":"2211.06786","title":"Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions","created":"2022-11-13","cited_by_count":29,"area":null},{"id":"2301.10022","title":"Koopman neural operator as a mesh-free solver of non-linear partial differential equations","created":"2023-01-24","cited_by_count":29,"area":null},{"id":"2307.05432","title":"Self-Supervised Learning with Lie Symmetries for Partial Differential Equations","created":"2023-07-11","cited_by_count":29,"area":null},{"id":"2412.04833","title":"Wavelet Diffusion Neural Operator","created":"2024-12-06","cited_by_count":29,"area":"diffusion"},{"id":"2306.13867","title":"Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems","created":"2023-06-24","cited_by_count":28,"area":null},{"id":"2503.22600","title":"Generative Latent Neural PDE Solver using Flow Matching","created":"2025-03-28","cited_by_count":28,"area":"diffusion"},{"id":"2402.12365","title":"Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators","created":"2024-02-19","cited_by_count":27,"area":null},{"id":"2307.12617","title":"Predicting Ordinary Differential Equations with Transformers","created":"2023-07-24","cited_by_count":26,"area":null},{"id":"2107.10879","title":"Discovering Sparse Interpretable Dynamics from Partial Observations","created":"2021-07-22","cited_by_count":25,"area":null},{"id":"2201.04976","title":"Data-Driven Modeling and Prediction of Non-Linearizable Dynamics via Spectral Submanifolds","created":"2022-01-13","cited_by_count":25,"area":null},{"id":"2202.08004","title":"Deep Koopman Operator with Control for Nonlinear Systems","created":"2022-02-16","cited_by_count":25,"area":null},{"id":"2303.17264","title":"Multifactor Sequential Disentanglement via Structured Koopman Autoencoders","created":"2023-03-30","cited_by_count":24,"area":null},{"id":"2310.17790","title":"Neural Stress Fields for Reduced-order Elastoplasticity and Fracture","created":"2023-10-26","cited_by_count":24,"area":null},{"id":"2306.10168","title":"Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis","created":"2023-06-16","cited_by_count":23,"area":null},{"id":"2311.04128","title":"Generative learning for nonlinear dynamics","created":"2023-11-07","cited_by_count":22,"area":null},{"id":"2209.02244","title":"The mpEDMD Algorithm for Data-Driven Computations of Measure-Preserving Dynamical Systems","created":"2022-09-06","cited_by_count":21,"area":null},{"id":"2303.17078","title":"Machine Learning for Partial Differential Equations","created":"2023-03-30","cited_by_count":21,"area":null},{"id":"2309.15421","title":"Deep Learning in Deterministic Computational Mechanics","created":"2023-09-27","cited_by_count":21,"area":null},{"id":"2401.11611","title":"Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks","created":"2024-01-21","cited_by_count":20,"area":null},{"id":"2501.13329","title":"Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks","created":"2025-01-23","cited_by_count":20,"area":null},{"id":"2505.13192","title":"True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics","created":"2025-05-19","cited_by_count":20,"area":null},{"id":"2108.04433","title":"Deep Learning Enhanced Dynamic Mode Decomposition","created":"2021-08-10","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":"2410.14240","title":"Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction","created":"2024-10-18","cited_by_count":19,"area":null},{"id":"2507.02608","title":"Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation","created":"2025-07-03","cited_by_count":19,"area":"diffusion"},{"id":"2110.06509","title":"Learning Stable Koopman Embeddings","created":"2021-10-13","cited_by_count":18,"area":null},{"id":"2211.03134","title":"WeakIdent: Weak formulation for Identifying Differential Equations using Narrow-fit and Trimming","created":"2022-11-06","cited_by_count":18,"area":null},{"id":"2302.03271","title":"IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning","created":"2023-02-07","cited_by_count":18,"area":null},{"id":"2307.09912","title":"Learning invariant representations of time-homogeneous stochastic dynamical systems","created":"2023-07-19","cited_by_count":17,"area":null},{"id":"2407.09645","title":"Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey","created":"2024-07-12","cited_by_count":16,"area":"deeprl"},{"id":"2410.02113","title":"Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for PDEs","created":"2024-10-03","cited_by_count":16,"area":"ssm"},{"id":"2301.10321","title":"Learning Dynamical Systems from Data: A Simple Cross-Validation Perspective, Part V: Sparse Kernel Flows for 132 Chaotic Dynamical Systems","created":"2023-01-24","cited_by_count":15,"area":null},{"id":"2306.04520","title":"Estimating Koopman operators with sketching to provably learn large scale dynamical systems","created":"2023-06-07","cited_by_count":15,"area":null},{"id":"2309.12991","title":"Deep learning probability flows and entropy production rates in active matter","created":"2023-09-22","cited_by_count":15,"area":null},{"id":"2403.12335","title":"Temporally Consistent Koopman Autoencoders for Forecasting Dynamical Systems","created":"2024-03-19","cited_by_count":15,"area":null},{"id":"2202.00368","title":"Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space","created":"2022-02-01","cited_by_count":14,"area":null},{"id":"2212.07892","title":"Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics","created":"2022-12-15","cited_by_count":14,"area":null},{"id":"2301.11961","title":"Reduced-Order Autodifferentiable Ensemble Kalman Filters","created":"2023-01-27","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":"2103.03385","title":"Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data","created":"2021-03-04","cited_by_count":13,"area":null},{"id":"2104.04785","title":"Generating Physically-Consistent Satellite Imagery for Climate Visualizations","created":"2021-04-10","cited_by_count":13,"area":null},{"id":"2110.02083","title":"Applying Machine Learning to Study Fluid Mechanics","created":"2021-10-05","cited_by_count":13,"area":null},{"id":"2101.05834","title":"Physics-aware, probabilistic model order reduction with guaranteed stability","created":"2021-01-14","cited_by_count":12,"area":null},{"id":"2102.07824","title":"An Operator Theoretic Approach for Analyzing Sequence Neural Networks","created":"2021-02-15","cited_by_count":12,"area":null},{"id":"2108.04085","title":"Bayesian Deep Learning for Partial Differential Equation Parameter Discovery with Sparse and Noisy Data","created":"2021-08-05","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":"2210.08959","title":"Flipped Classroom: Effective Teaching for Time Series Forecasting","created":"2022-10-17","cited_by_count":12,"area":null},{"id":"2211.07768","title":"Meta-Learning of Neural State-Space Models Using Data From Similar Systems","created":"2022-11-14","cited_by_count":12,"area":null},{"id":"2304.03376","title":"Interpretable statistical representations of neural population dynamics and geometry","created":"2023-04-06","cited_by_count":12,"area":null},{"id":"2306.17396","title":"Koopman operator learning using invertible neural networks","created":"2023-06-30","cited_by_count":12,"area":null},{"id":"2310.10745","title":"Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder","created":"2023-10-16","cited_by_count":12,"area":null},{"id":"2405.11470","title":"VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting","created":"2024-05-19","cited_by_count":12,"area":null},{"id":"2410.01990","title":"Deep Learning Alternatives of the Kolmogorov Superposition Theorem","created":"2024-10-02","cited_by_count":12,"area":null},{"id":"2512.24440","title":"Towards mechanistic understanding in a data-driven weather model: internal activations reveal interpretable physical features","created":"2025-12-30","cited_by_count":12,"area":null},{"id":"2109.12390","title":"Model reduction for the material point method via an implicit neural representation of the deformation map","created":"2021-09-25","cited_by_count":11,"area":null},{"id":"2110.02583","title":"Deep Identification of Nonlinear Systems in Koopman Form","created":"2021-10-06","cited_by_count":11,"area":null},{"id":"2201.05098","title":"Neural Koopman Lyapunov Control","created":"2022-01-13","cited_by_count":11,"area":null},{"id":"2302.11101","title":"Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecasts","created":"2023-02-22","cited_by_count":11,"area":null},{"id":"2309.05812","title":"Interpretable learning of effective dynamics for multiscale systems","created":"2023-09-11","cited_by_count":11,"area":null}],"next_cursor":"100"}