Cover Image

End-to-End Pseudo-Measurement Learning for State Estimation under Limited Observability

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	De la Varga, J. G., Pineda, S., Stratigakos, A., Morales, J. M.
Format:	Preprint
Published:	2026
Subjects:	Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Robust Contextual State Estimation with Limited Measurement Data
by: De la Varga, J. G., et al.
Published: (2025)

Learning-based State Estimation in Distribution Systems with Limited Real-Time Measurements
by: De la Varga, J. G., et al.
Published: (2023)

Constructing Deployment Scenarios for Reserve Deliverability via Adaptive Robust Optimization
by: Van Caelenberg, Guillaume, et al.
Published: (2026)

Soft-Radial Projection for Constrained End-to-End Learning
by: Schneider, Philipp J., et al.
Published: (2026)

End-to-End Learning of Correlated Operating Reserve Requirements in Security-Constrained Economic Dispatch
by: Shen, Owen, et al.
Published: (2026)

Distributionally Robust Control with End-to-End Statistically Guaranteed Metric Learning
by: Wu, Jingyi, et al.
Published: (2025)

End-to-End Learning Framework for Solving Non-Markovian Optimal Control
by: Zhang, Xiaole, et al.
Published: (2025)

Two-Stage Data-Driven Contextual Robust Optimization: An End-to-End Learning Approach for Online Energy Applications
by: Gamboa, Carlos, et al.
Published: (2025)

Decision-calibrated prediction sets for robust power system operations
by: Stratigakos, Akylas, et al.
Published: (2026)

Wasserstein Distributionally Robust Control and State Estimation for Partially Observable Linear Systems
by: Jang, Minhyuk, et al.
Published: (2024)

Sobolev Training of End-to-End Optimization Proxies
by: Rosemberg, Andrew W., et al.
Published: (2025)

End-to-End Reinforcement Learning of Koopman Models for eNMPC of an Air Separation Unit
by: Mayfrank, Daniel, et al.
Published: (2025)

End-to-End Conformal Calibration for Optimization Under Uncertainty
by: Yeh, Christopher, et al.
Published: (2024)

AI2STOW: End-to-End Deep Reinforcement Learning to Construct Master Stowage Plans under Demand Uncertainty
by: Van Twiller, Jaike, et al.
Published: (2025)

End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing
by: Arya, Gaurav, et al.
Published: (2022)

ResearchEVO: An End-to-End Framework for Automated Scientific Discovery and Documentation
by: Zhao, Zhe, et al.
Published: (2026)

Dynamic Interval Scheduling with Random Start and End Times
by: Gong, Rui, et al.
Published: (2026)

An Autonomous, End-to-End, Convex-Based Framework for Close-Range Rendezvous Trajectory Design and Guidance with Hardware Testbed Validation
by: Wijayatunga, Minduli C., et al.
Published: (2026)

Stochastic Differential Dynamic Programming for Trajectory Optimization under Partial Observability
by: Fujiwara, Masahiro, et al.
Published: (2026)

End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
by: Ma, Shaocong, et al.
Published: (2024)

End-to-End Training of High-Dimensional Optimal Control with Implicit Hamiltonians via Jacobian-Free Backpropagation
by: Gelphman, Eric, et al.
Published: (2025)

Learning-Assisted Optimization for Transmission Switching
by: Pineda, Salvador, et al.
Published: (2023)

State-of-the-art Methods for Pseudo-Boolean Solving with SCIP
by: Mexi, Gioni, et al.
Published: (2025)

The Sweet Spot of Bound Tightening for Topology Optimization
by: Pineda, Salvador, et al.
Published: (2025)

Multi-Armed Sampling Problem and the End of Exploration
by: Pedramfar, Mohammad, et al.
Published: (2025)

Secure Set-based State Estimation for Safety-Critical Applications under Adversarial Attacks on Sensors
by: Niazi, M. Umar B., et al.
Published: (2023)

Theory for Optimal Estimation and Control under Resource Limitations and Its Applications to Biological Information Processing and Decision-Making
by: Tottori, Takehiro, et al.
Published: (2024)

Sensitivity of Filter Kernels and Robustness Bounds to Transition and Measurement Kernel Perturbations in Partially Observable Stochastic Control
by: Demirci, Yunus Emre, et al.
Published: (2025)

City-LEO: Toward Transparent City Management Using LLM with End-to-End Optimization
by: Jiao, Zihao, et al.
Published: (2024)

SPO-VCS: An End-to-End Smart Predict-then-Optimize Framework with Alternating Differentiation Method for Relocation Problems in Large-Scale Vehicle Crowd Sensing
by: Wang, Xinyu, et al.
Published: (2024)

Muon Outperforms Adam in Tail-End Associative Memory Learning
by: Wang, Shuche, et al.
Published: (2025)

Controllability and Observability Imply Exponential Decay of Sensitivity in Dynamic Optimization
by: Shin, Sungho, et al.
Published: (2021)

Learning-based Attitude Estimation with Noisy Measurements and Unknown Gyro Bias
by: Oveissi, Parham, et al.
Published: (2024)

DOGE-Train: Discrete Optimization on GPU with End-to-end Training
by: Abbas, Ahmed, et al.
Published: (2022)

Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow
by: Pineda, Salvador, et al.
Published: (2024)

Data-Boosted Optimization for AC Optimal Power Flow: Interior-Point and Spatial Branching Methods
by: Repiso, Ignacio, et al.
Published: (2025)

Minimax Performance Limits for Multiple-Model Estimation
by: Kjellqvist, Olle
Published: (2023)

Offline and Online Nonlinear Inverse Differential Games with Known and Approximated Cost and Value Function Structures
by: Karg, Philipp, et al.
Published: (2024)

Pseudo basic steps: Bound improvement guarantees from Lagrangian decomposition in convex disjunctive programming
by: Papageorgiou, Dimitri J., et al.
Published: (2025)

Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference
by: Keshvari, Arvin, et al.
Published: (2026)