{"total":51,"rows":[{"id":"2101.00554","title":"Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning","created":"2021-01-03","cited_by_count":57,"area":null},{"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":"2301.10937","title":"Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows","created":"2023-01-26","cited_by_count":31,"area":null},{"id":"2303.15832","title":"The transformative potential of machine learning for experiments in fluid mechanics","created":"2023-03-28","cited_by_count":27,"area":null},{"id":"2302.12682","title":"A DeepONet multi-fidelity approach for residual learning in reduced order modeling","created":"2023-02-24","cited_by_count":14,"area":null},{"id":"2307.11793","title":"Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction","created":"2023-07-20","cited_by_count":12,"area":null},{"id":"2505.18190","title":"PhySense: Sensor Placement Optimization for Accurate Physics Sensing","created":"2025-05-19","cited_by_count":11,"area":null},{"id":"2102.13476","title":"PySensors: A Python Package for Sparse Sensor Placement","created":"2021-02-20","cited_by_count":8,"area":null},{"id":"2108.08298","title":"A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systems","created":"2021-08-17","cited_by_count":8,"area":null},{"id":"2306.13637","title":"Constrained optimization of sensor placement for nuclear digital twins","created":"2023-06-23","cited_by_count":6,"area":null},{"id":"2107.09256","title":"Active operator inference for learning low-dimensional dynamical-system models from noisy data","created":"2021-07-20","cited_by_count":5,"area":null},{"id":"2107.10030","title":"Differentiable Feature Selection, a Reparameterization Approach","created":"2021-07-21","cited_by_count":4,"area":null},{"id":"2212.07102","title":"Artificial intelligence-driven digital twin of a modern house demonstrated in virtual reality","created":"2022-12-14","cited_by_count":4,"area":null},{"id":"2101.11162","title":"Inadequacy of Linear Methods for Minimal Sensor Placement and Feature Selection in Nonlinear Systems; a New Approach Using Secants","created":"2021-01-27","cited_by_count":3,"area":null},{"id":"2307.11838","title":"Data-Induced Interactions of Sparse Sensors Using Statistical Physics","created":"2023-07-21","cited_by_count":3,"area":null},{"id":"2603.12635","title":"Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems","created":"2026-03-13","cited_by_count":3,"area":null},{"id":"2105.00400","title":"Model discovery in the sparse sampling regime","created":"2021-05-02","cited_by_count":2,"area":null},{"id":"2202.11701","title":"Super-resolution GANs of randomly-seeded fields","created":"2022-02-23","cited_by_count":2,"area":null},{"id":"2405.08613","title":"GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of Nonlinear Partial Differential Equations","created":"2024-05-14","cited_by_count":2,"area":null},{"id":"2411.05887","title":"Predictive Digital Twin for Condition Monitoring Using Thermal Imaging","created":"2024-11-08","cited_by_count":2,"area":null},{"id":"2512.19309","title":"Time-Vertex Machine Learning for Optimal Sensor Placement in Temporal Graph Signals: Applications in Structural Health Monitoring","created":"2025-12-22","cited_by_count":2,"area":null},{"id":"2312.09176","title":"Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization","created":"2023-12-14","cited_by_count":1,"area":null},{"id":"2403.19721","title":"Computationally and Memory-Efficient Robust Predictive Analytics Using Big Data","created":"2024-03-27","cited_by_count":1,"area":null},{"id":"2407.16058","title":"Revisiting Score Function Estimators for $k$-Subset Sampling","created":"2024-07-22","cited_by_count":1,"area":null},{"id":"2409.17825","title":"Physics-aligned Schr\\\"{o}dinger bridge","created":"2024-09-26","cited_by_count":1,"area":null},{"id":"2410.15982","title":"State Estimation Using Sparse DEIM and Recurrent Neural Networks","created":"2024-10-21","cited_by_count":1,"area":null},{"id":"2411.12948","title":"Attention-Based Reconstruction of Full-Field Tsunami Waves from Sparse Tsunameter Networks","created":"2024-11-20","cited_by_count":1,"area":null},{"id":"2510.21852","title":"Interpretable Diagnostics and Adaptive Data Assimilation for Neural ODEs via Discrete Empirical Interpolation","created":"2025-10-22","cited_by_count":1,"area":null},{"id":"2603.13077","title":"Rooftop Wind Field Reconstruction Using Sparse Sensors: From Deterministic to Generative Learning Methods","created":"2026-03-13","cited_by_count":1,"area":null},{"id":"2604.01305","title":"UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engression","created":"2026-04-01","cited_by_count":1,"area":null},{"id":"2605.18866","title":"FLUIDSPLAT: Reconstructing Physical Fields from Sparse Sensors via Gaussian Primitives","created":"2026-05-15","cited_by_count":1,"area":null},{"id":"2111.06057","title":"Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity","created":"2021-11-11","cited_by_count":0,"area":null},{"id":"2206.13968","title":"Information Entropy Initialized Concrete Autoencoder for Optimal Sensor Placement and Reconstruction of Geophysical Fields","created":"2022-06-28","cited_by_count":0,"area":null},{"id":"2303.12260","title":"Information-Based Sensor Placement for Data-Driven Estimation of Unsteady Flows","created":"2023-03-22","cited_by_count":0,"area":null},{"id":"2306.04518","title":"Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing","created":"2023-06-07","cited_by_count":0,"area":null},{"id":"2409.18423","title":"A physics-driven sensor placement optimization methodology for temperature field reconstruction","created":"2024-09-27","cited_by_count":0,"area":null},{"id":"2508.03872","title":"Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training","created":"2025-08-05","cited_by_count":0,"area":null},{"id":"2510.22517","title":"Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)","created":"2025-10-26","cited_by_count":0,"area":null},{"id":"2511.04556","title":"Flow Reconstruction from Sparse Measurements in Urban Drainage Networks: An Application and Evaluation of Data-Driven Sparse Sensing","created":"2025-11-06","cited_by_count":0,"area":null},{"id":"2602.00386","title":"Generalized Inverses of Matrix Products: From Fundamental Subspaces to Randomized Decompositions","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2602.23188","title":"Efficient Adaptation of ROMs for Unsteady Flows Using Data Assimilation","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2603.03650","title":"Adaptive Sensing of Continuous Physical Systems for Machine Learning","created":"2026-03-04","cited_by_count":0,"area":null},{"id":"2604.01216","title":"LAtent Phase Inference from Short time sequences using SHallow REcurrent Decoders (LAPIS-SHRED)","created":"2026-04-01","cited_by_count":0,"area":null},{"id":"2604.24912","title":"Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2605.23712","title":"Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2606.00078","title":"Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2606.03936","title":"Correcting Neural Operator Spectral Bias via Diffusion Posterior Sampling with Sparse Observations","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.12182","title":"How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2606.24989","title":"Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applications","created":"2026-06-23","cited_by_count":0,"area":null},{"id":"2607.26414","title":"Origins and mitigation of double descent in reduced order modeling","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2609.20857","title":"Reconstruction of 4D Mitral Regurgitation Hemodynamics from Sparse Planar Data using Deep Operator Networks with Test-Time Adaptation","created":"2026-08-31","cited_by_count":0,"area":null}],"next_cursor":null}