Cover Image

Reconstructing Multi-Scale Physical Fields from Extremely Sparse Measurements with an Autoencoder-Diffusion Cascade

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yi, Letian, Zhang, Tingpeng, Zhou, Mingyuan, Wang, Guannan, Su, Quanke, Lai, Zhilu
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Applied Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries
by: Li, Zhihao, et al.
Published: (2024)

Mechanical in-sensor computing: a programmable meta-sensor for structural damage classification without external electronic power
by: Zhang, Tingpeng, et al.
Published: (2025)

Improving Sparse Autoencoder with Dynamic Attention
by: Wang, Dongsheng, et al.
Published: (2026)

Transforming physics-informed machine learning to convex optimization
by: Yi, Letian, et al.
Published: (2025)

On the Rate of Convergence of Kolmogorov-Arnold Network Regression Estimators
by: Liu, Wei, et al.
Published: (2025)

Gemini & Physical World: Large Language Models Can Estimate the Intensity of Earthquake Shaking from Multi-Modal Social Media Posts
by: Mousavi, S. Mostafa, et al.
Published: (2024)

PoreDiT: A Scalable Generative Model for Large-Scale Digital Rock Reconstruction
by: Huang, Yizhuo, et al.
Published: (2026)

AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction
by: Hettige, Kethmi Hirushini, et al.
Published: (2024)

Learning Multi-Level Features with Matryoshka Sparse Autoencoders
by: Bussmann, Bart, et al.
Published: (2025)

Superposition as Lossy Compression: Measure with Sparse Autoencoders and Connect to Adversarial Vulnerability
by: Bereska, Leonard, et al.
Published: (2025)

Sparse Autoencoders, Again?
by: Lu, Yin, et al.
Published: (2025)

SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders
by: Cywiński, Bartosz, et al.
Published: (2025)

Multi-Task Deep Learning for Surface Metrology
by: Kucharski, D., et al.
Published: (2025)

Diffusion Boosted Trees
by: Han, Xizewen, et al.
Published: (2024)

From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators
by: Li, Zhihao, et al.
Published: (2026)

Beyond Redundancy: Diverse and Specialized Multi-Expert Sparse Autoencoder
by: Xu, Zhen, et al.
Published: (2025)

Are Sparse Autoencoder Benchmarks Reliable?
by: Chanin, David
Published: (2026)

Physics-Enhanced Multi-fidelity Learning for Optical Surface Imprint
by: Chen, Yongchao
Published: (2023)

ReSAE: Residualized Sparse Autoencoders for Multi-Layer Transformer Interventions
by: Poduval, Prathyush, et al.
Published: (2026)

Diffusion Policies creating a Trust Region for Offline Reinforcement Learning
by: Chen, Tianyu, et al.
Published: (2024)

BatchTopK Sparse Autoencoders
by: Bussmann, Bart, et al.
Published: (2024)

Spatially-Aware Diffusion Models with Cross-Attention for Global Field Reconstruction with Sparse Observations
by: Zhuang, Yilin, et al.
Published: (2024)

Are Sparse Autoencoders Useful? A Case Study in Sparse Probing
by: Kantamneni, Subhash, et al.
Published: (2025)

Residualized Temporal Sparse Autoencoders for Interpreting Diffusion Models
by: Yeung, Calvin, et al.
Published: (2026)

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

Adaptive Sparse Allocation with Mutual Choice & Feature Choice Sparse Autoencoders
by: Ayonrinde, Kola
Published: (2024)

CHLU: The Causal Hamiltonian Learning Unit as a Symplectic Primitive for Deep Learning
by: Jawahar, Pratik, et al.
Published: (2026)

Reinforcement learning for spin torque oscillator tasks
by: Mojsiejuk, Jakub, et al.
Published: (2025)

AIFL: A Global Daily Streamflow Forecasting Model Using Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS
by: Taccari, Maria Luisa, et al.
Published: (2026)

Uncertainty-Calibrated Spatiotemporal Field Diffusion with Sparse Supervision
by: Valencia, Kevin, et al.
Published: (2026)

Empirical Evaluation of Progressive Coding for Sparse Autoencoders
by: Peter, Hans, et al.
Published: (2025)

Do Sparse Autoencoders Capture Concept Manifolds?
by: Bhalla, Usha, et al.
Published: (2026)

Improving Dictionary Learning with Gated Sparse Autoencoders
by: Rajamanoharan, Senthooran, et al.
Published: (2024)

On the transferability of Sparse Autoencoders for interpreting compressed models
by: Gupte, Suchit, et al.
Published: (2025)

Data Whitening Improves Sparse Autoencoder Learning
by: Saraswatula, Ashwin, et al.
Published: (2025)

Scaling Speech Tokenizers with Diffusion Autoencoders
by: Wang, Yuancheng, et al.
Published: (2026)

DLM-Scope: Mechanistic Interpretability of Diffusion Language Models via Sparse Autoencoders
by: Wang, Xu, et al.
Published: (2026)

The Geometric Wall: Manifold Structure Predicts Layerwise Sparse Autoencoder Scaling Laws
by: Zaher, Eslam, et al.
Published: (2026)

Sparsely Supervised Diffusion
by: Zhao, Wenshuai, et al.
Published: (2026)

Fully Analog Resonant Recurrent Neural Network via Metacircuit
by: Zhou, Zixin, et al.
Published: (2026)