Imagen de Portada

Physics-informed Blind Reconstruction of Dense Fields from Sparse Measurements using Neural Networks with a Differentiable Simulator

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Aloni, Ofek, Fishbain, Barak
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Synthetic Random Environmental Time Series Generation with Similarity Control, Preserving Original Signal's Statistical Characteristics
por: Aloni, Ofek, et al.
Publicado: (2025)

Wave (from) Polarized Light Learning (WPLL) method: high resolution spatio-temporal measurements of water surface waves in laboratory setups
por: Ginio, Noam, et al.
Publicado: (2024)

Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear array
por: Ginio, Noam, et al.
Publicado: (2024)

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
por: Amran, Ofek, et al.
Publicado: (2026)

Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks
por: Luo, Xihaier, et al.
Publicado: (2024)

Automatic Stability and Recovery for Neural Network Training
por: Or, Barak
Publicado: (2026)

Physics-informed Neural Networks with Unknown Measurement Noise
por: Pilar, Philipp, et al.
Publicado: (2022)

Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models
por: Cohen, Aloni
Publicado: (2025)

Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data
por: Chu, Mengyu, et al.
Publicado: (2022)

Barriers to Counterfactual Credit Attribution for Autoregressive Models
por: Cohen, Aloni, et al.
Publicado: (2026)

Reconstructing Multi-Scale Physical Fields from Extremely Sparse Measurements with an Autoencoder-Diffusion Cascade
por: Yi, Letian, et al.
Publicado: (2025)

Implicit Bias of Mirror Flow in Homogeneous Neural Networks: Sparse and Dense Feature Learning
por: Jacobs, Tom, et al.
Publicado: (2026)

Learning to Reconstruct Temperature Field from Sparse Observations with Implicit Physics Priors
por: Li, Shihang, et al.
Publicado: (2025)

Uncertainty-aware Physics-informed Neural Networks for Robust CARS-to-Raman Signal Reconstruction
por: Venkataramanan, Aishwarya, et al.
Publicado: (2025)

Densely Multiplied Physics Informed Neural Networks
por: Jiang, Feilong, et al.
Publicado: (2024)

Learning a Sparse Neural Network using IHT
por: Damadi, Saeed, et al.
Publicado: (2024)

Sparse-to-Field Reconstruction via Stochastic Neural Dynamic Mode Decomposition
por: Kim, Yujin, et al.
Publicado: (2025)

Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks
por: Zhai, Jucai, et al.
Publicado: (2026)

Dense-Sparse Deep Convolutional Neural Networks Training for Image Denoising
por: Alawode, Basit O., et al.
Publicado: (2021)

MTL-FNO: A Lightweight Multi-Task Fourier Neural Operator for Sparse Field Reconstruction
por: Ye, Siyu, et al.
Publicado: (2026)

Dynamic Reconstruction of Ultrasound-Derived Flow Fields With Physics-Informed Neural Fields
por: Patel, Viraj, et al.
Publicado: (2025)

Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach
por: Zhang, Qian, et al.
Publicado: (2026)

Antenna Near-Field Reconstruction from Far-Field Data Using Convolutional Neural Networks
por: Bagherkhani, Sahar, et al.
Publicado: (2025)

Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction
por: Zhang, Huiwen, et al.
Publicado: (2026)

Attention-Based Reconstruction of Full-Field Tsunami Waves from Sparse Tsunameter Networks
por: McDugald, Edward, et al.
Publicado: (2024)

Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements
por: Dang, Hiep Vo, et al.
Publicado: (2024)

Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
por: Wang, Sifan, et al.
Publicado: (2025)

Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
por: Bhaila, Karuna, et al.
Publicado: (2023)

Neural Networks With Dense Weights Are Not Universal Approximators
por: Rauchwerger, Levi, et al.
Publicado: (2026)

Sparse-Input Neural Network using Group Concave Regularization
por: Luo, Bin, et al.
Publicado: (2023)

PDE-aware Optimizer for Physics-informed Neural Networks
por: Churiwala, Vismay, et al.
Publicado: (2025)

Physics-Informed Neural Network Approaches for Sparse Data Flow Reconstruction of Unsteady Flow Around Complex Geometries
por: Malineni, Vamsi Sai Krishna, et al.
Publicado: (2025)

Disentangling Dense Embeddings with Sparse Autoencoders
por: O'Neill, Charles, et al.
Publicado: (2024)

Physics-informed Neural Networks for Encoding Dynamics in Real Physical Systems
por: Alsharif, Hamza
Publicado: (2024)

Sparse Covariance Neural Networks
por: Cavallo, Andrea, et al.
Publicado: (2024)

Emergence of Computational Structure in a Neural Network Physics Simulator
por: Hitchcock, Rohan, et al.
Publicado: (2025)

Verifying Physics-Informed Neural Network Fidelity using Classical Fisher Information from Differentiable Dynamical System
por: Filho, Josafat Ribeiro Leal, et al.
Publicado: (2026)

Differentiability in Unrolled Training of Neural Physics Simulators on Transient Dynamics
por: List, Bjoern, et al.
Publicado: (2024)

Uncertainty-Aware Graph Neural Reconstruction of Urban Temperature Fields from Sparse Sensors under Deployment Constraints
por: Snaiki, Reda, et al.
Publicado: (2026)

XNNTab -- Interpretable Neural Networks for Tabular Data using Sparse Autoencoders
por: Elhadri, Khawla, et al.
Publicado: (2025)