{"total":58,"rows":[{"id":"2309.15325","title":"Neural Operators for Accelerating Scientific Simulations and Design","created":"2023-09-27","cited_by_count":221,"area":null},{"id":"2406.17763","title":"DiffusionPDE: Generative PDE-Solving Under Partial Observation","created":"2024-06-25","cited_by_count":108,"area":"diffusion"},{"id":"2306.07266","title":"Operator Learning with Neural Fields: Tackling PDEs on General Geometries","created":"2023-06-12","cited_by_count":84,"area":null},{"id":"2209.14855","title":"Continuous PDE Dynamics Forecasting with Implicit Neural Representations","created":"2022-09-29","cited_by_count":73,"area":null},{"id":"2406.02176","title":"AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields","created":"2024-06-04","cited_by_count":39,"area":null},{"id":"2210.00124","title":"Implicit Neural Spatial Representations for Time-dependent PDEs","created":"2022-09-30","cited_by_count":35,"area":null},{"id":"2412.04833","title":"Wavelet Diffusion Neural Operator","created":"2024-12-06","cited_by_count":29,"area":"diffusion"},{"id":"2503.22600","title":"Generative Latent Neural PDE Solver using Flow Matching","created":"2025-03-28","cited_by_count":28,"area":"diffusion"},{"id":"2411.18197","title":"Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready 3D Characters","created":"2024-11-27","cited_by_count":23,"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":"2507.20065","title":"Geometric Operator Learning with Optimal Transport","created":"2025-07-26","cited_by_count":16,"area":null},{"id":"2402.14646","title":"CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations","created":"2024-02-22","cited_by_count":14,"area":null},{"id":"2406.03919","title":"Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations","created":"2024-06-06","cited_by_count":12,"area":null},{"id":"2506.10973","title":"Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning","created":"2025-06-12","cited_by_count":12,"area":null},{"id":"2306.15630","title":"Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes","created":"2023-06-27","cited_by_count":10,"area":null},{"id":"2311.04434","title":"A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space","created":"2023-11-08","cited_by_count":10,"area":null},{"id":"2210.13414","title":"Thermodynamics-informed neural networks for physically realistic mixed reality","created":"2022-10-24","cited_by_count":9,"area":null},{"id":"2309.09574","title":"Latent assimilation with implicit neural representations for unknown dynamics","created":"2023-09-18","cited_by_count":9,"area":null},{"id":"2312.05984","title":"Accurate Differential Operators for Hybrid Neural Fields","created":"2023-12-10","cited_by_count":9,"area":null},{"id":"2301.13331","title":"Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML","created":"2023-01-30","cited_by_count":8,"area":null},{"id":"2311.16410","title":"Reduced-order modeling for parameterized PDEs via implicit neural representations","created":"2023-11-28","cited_by_count":8,"area":null},{"id":"2405.14558","title":"FUSE: Fast Unified Simulation and Estimation for PDEs","created":"2024-05-23","cited_by_count":8,"area":null},{"id":"2308.12970","title":"NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory","created":"2023-08-24","cited_by_count":7,"area":null},{"id":"2510.15280","title":"Foundation Models for Scientific Discovery: From Paradigm Enhancement to Paradigm Transition","created":"2025-10-17","cited_by_count":6,"area":null},{"id":"2411.19125","title":"Advancing Generalization in PINNs through Latent-Space Representations","created":"2024-11-28","cited_by_count":5,"area":null},{"id":"2412.05233","title":"Physics-informed reduced order model with conditional neural fields","created":"2024-12-06","cited_by_count":4,"area":null},{"id":"2605.07079","title":"Learning Visual Feature-Based World Models via Residual Latent Action","created":"2026-05-08","cited_by_count":4,"area":"wm"},{"id":"2402.15636","title":"Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization","created":"2024-02-23","cited_by_count":3,"area":null},{"id":"2502.08683","title":"A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs","created":"2025-02-12","cited_by_count":3,"area":null},{"id":"2605.27852","title":"ClothTransformer: Unified Latent-Space Transformers for Scalable Cloth Simulation","created":"2026-05-27","cited_by_count":3,"area":null},{"id":"2605.30347","title":"NeuROK: Generative 4D Neural Object Kinematics","created":"2026-05-28","cited_by_count":3,"area":null},{"id":"2303.15827","title":"CONFIDE: Contextual Finite Differences Modelling of PDEs","created":"2023-03-28","cited_by_count":2,"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":"2407.03925","title":"Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order Reduction","created":"2024-07-04","cited_by_count":2,"area":null},{"id":"2409.00220","title":"Learning Latent Space Dynamics with Model-Form Uncertainties: A Stochastic Reduced-Order Modeling Approach","created":"2024-08-30","cited_by_count":2,"area":null},{"id":"2410.14952","title":"Accelerate Coastal Ocean Circulation Model with AI Surrogate","created":"2024-10-19","cited_by_count":2,"area":null},{"id":"2505.14595","title":"Physics-informed Reduced Order Modeling of Time-dependent PDEs via Differentiable Solvers","created":"2025-05-20","cited_by_count":2,"area":null},{"id":"2510.25135","title":"Conditional neural field for spatial dimension reduction of turbulence data: a comparison study","created":"2025-10-29","cited_by_count":2,"area":null},{"id":"2402.09234","title":"Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks","created":"2024-02-14","cited_by_count":1,"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":"2501.17296","title":"COMPOL: A Unified Neural Operator Framework for Scalable Multi-Physics Simulations","created":"2025-01-28","cited_by_count":1,"area":null},{"id":"2506.06858","title":"FA-INR: Adaptive Implicit Neural Representations for Interpretable Exploration of Simulation Ensembles","created":"2025-06-07","cited_by_count":1,"area":"moe"},{"id":"2602.01397","title":"Nonlinear model reduction for transport-dominated problems","created":"2026-02-01","cited_by_count":1,"area":null},{"id":"2602.18248","title":"Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver","created":"2026-02-20","cited_by_count":1,"area":null},{"id":"2603.00360","title":"KROM: Kernelized Reduced Order Modeling","created":"2026-02-27","cited_by_count":1,"area":null},{"id":"2603.12676","title":"Parameterized Temporal Extrapolation of PDEs via Disentangled Latent Dynamics Manifold Fusion","created":"2026-03-13","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":"2310.06644","title":"HYVE: Hybrid Vertex Encoder for Neural Distance Fields","created":"2023-10-10","cited_by_count":0,"area":null},{"id":"2504.15657","title":"Neural Kinematic Bases for Fluids","created":"2025-04-22","cited_by_count":0,"area":null},{"id":"2510.15041","title":"Generalized Dynamics Generation towards Scannable Physical World Model","created":"2025-10-16","cited_by_count":0,"area":"wm"},{"id":"2601.00038","title":"Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference","created":"2025-12-30","cited_by_count":0,"area":null},{"id":"2603.23194","title":"PhysSkin: Real-Time and Generalizable Physics-Based Animation via Self-Supervised Neural Skinning","created":"2026-03-24","cited_by_count":0,"area":null},{"id":"2605.25115","title":"Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition","created":"2026-05-24","cited_by_count":0,"area":null},{"id":"2606.15053","title":"Physics-conforming Latent Twins","created":"2026-06-13","cited_by_count":0,"area":null},{"id":"2609.14926","title":"Linearized PINN with pretrained nonlinear layers","created":"2026-09-14","cited_by_count":0,"area":null},{"id":"2609.31573","title":"How Far Can INRs Go? Cross-Domain Parameter-efficient INR-Based Semantic Segmentation for Brain MRI","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.33205","title":"SMORE: Stability-Promoting Mesh-Agnostic Model Reduction for Time-Dependent PDEs","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.34715","title":"PDE-JEPA: Predictive Representation Learning of Latent Dynamics Modeling for Parametric PDEs","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}