{"total":32,"rows":[{"id":"2209.15616","title":"Towards Multi-spatiotemporal-scale Generalized PDE Modeling","created":"2022-09-30","cited_by_count":197,"area":null},{"id":"2305.08809","title":"Interpretability at Scale: Identifying Causal Mechanisms in Alpaca","created":"2023-05-15","cited_by_count":103,"area":null},{"id":"2209.04934","title":"Clifford Neural Layers for PDE Modeling","created":"2022-09-08","cited_by_count":92,"area":null},{"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,"area":null},{"id":"2408.12171","title":"Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey","created":"2024-08-22","cited_by_count":28,"area":null},{"id":"2305.01122","title":"Learning Controllable Adaptive Simulation for Multi-resolution Physics","created":"2023-05-01","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":"2408.15183","title":"On latent dynamics learning in nonlinear reduced order modeling","created":"2024-08-27","cited_by_count":14,"area":null},{"id":"2402.08383","title":"Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution","created":"2024-02-13","cited_by_count":8,"area":null},{"id":"2401.09198","title":"Space and Time Continuous Physics Simulation From Partial Observations","created":"2024-01-17","cited_by_count":6,"area":null},{"id":"2305.07671","title":"LatentPINNs: Generative physics-informed neural networks via a latent representation learning","created":"2023-05-11","cited_by_count":5,"area":null},{"id":"2304.01732","title":"Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systems","created":"2023-04-04","cited_by_count":4,"area":null},{"id":"2412.20962","title":"Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction","created":"2024-12-30","cited_by_count":4,"area":null},{"id":"2307.04110","title":"Learning Space-Time Continuous Neural PDEs from Partially Observed States","created":"2023-07-09","cited_by_count":3,"area":null},{"id":"2512.08987","title":"3DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimization","created":"2025-12-06","cited_by_count":3,"area":null},{"id":"2406.00368","title":"Learning Spatiotemporal Dynamical Systems from Point Process Observations","created":"2024-06-01","cited_by_count":2,"area":null},{"id":"2410.11617","title":"M$^{2}$M: Learning controllable Multi of experts and multi-scale operators are the Partial Differential Equations need","created":"2024-10-01","cited_by_count":2,"area":null},{"id":"2510.00233","title":"Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2402.16913","title":"PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations","created":"2024-02-25","cited_by_count":1,"area":null},{"id":"2605.10154","title":"Stable Long-Horizon PDE Forecasting via Latent Structured Spectral Propagators","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2606.23359","title":"Adaptive Hard-Soft Physics-Informed Neural Networks for Robust Boundary-Constrained PDE Solving","created":"2026-06-22","cited_by_count":1,"area":null},{"id":"2607.01128","title":"GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems","created":"2026-07-01","cited_by_count":1,"area":null},{"id":"2210.01123","title":"Towards Learned Simulators for Cell Migration","created":"2022-10-02","cited_by_count":0,"area":null},{"id":"2312.09460","title":"Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics","created":"2023-11-27","cited_by_count":0,"area":null},{"id":"2502.08784","title":"Acoustic Wave Manipulation Through Sparse Robotic Actuation","created":"2025-02-12","cited_by_count":0,"area":null},{"id":"2505.16041","title":"Physics-based machine learning for mantle convection simulations","created":"2025-05-21","cited_by_count":0,"area":null},{"id":"2506.12996","title":"Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material","created":"2025-06-15","cited_by_count":0,"area":null},{"id":"2606.00892","title":"An Exploratory Study into using Machine-Learning for Fast Step-by-step Emulation of Numerical Mechanical Thrombectomy Simulations for Ischemic Stroke","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2607.21644","title":"Toward Goal-Agnostic Joint-Embedding Predictive Control of Partial Differential Equations","created":"2026-07-21","cited_by_count":0,"area":null},{"id":"2609.20309","title":"Hypernetwork-Parameterized Spatially Adaptive Neural Operators for PDE Learning","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2609.34070","title":"FLARE: Flow Matching with Local Axis-Angle Representations for Stochastic Micromagnetic Evolution","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.14537","title":"Learn the Dynamics, Enforce the Physics: Generative Latent Operators for High-Dimensional Beam Evolution","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}