Copertina

Learning Generative Models for Climbing Aircraft from Radar Data

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Pepper, Nick, Thomas, Marc
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Systems and Control Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Conditioning Aircraft Trajectory Prediction on Meteorological Data with a Physics-Informed Machine Learning Approach
di: Hodgkin, Amy, et al.
Pubblicazione: (2026)

Probabilistic Simulation of Aircraft Descent via a Physics-Informed Machine Learning Approach
di: Hodgkin, Amy, et al.
Pubblicazione: (2025)

Hypernetwork-Conditioned Reinforcement Learning for Robust Control of Fixed-Wing Aircraft under Actuator Failures
di: Marquis, Dennis, et al.
Pubblicazione: (2026)

BUILDA: A Thermal Building Data Generation Framework for Transfer Learning
di: Krug, Thomas, et al.
Pubblicazione: (2025)

Multi-Target Radar Search and Track Using Sequence-Capable Deep Reinforcement Learning
di: Ewers, Jan-Hendrik, et al.
Pubblicazione: (2025)

Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
di: Ewering, Jan-Hendrik, et al.
Pubblicazione: (2025)

BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control
di: Koch, Felix, et al.
Pubblicazione: (2026)

GenTL: A General Transfer Learning Model for Building Thermal Dynamics
di: Raisch, Fabian, et al.
Pubblicazione: (2025)

An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data
di: Sever, Gulay Goktas, et al.
Pubblicazione: (2023)

A Graph-Enhanced Deep-Reinforcement Learning Framework for the Aircraft Landing Problem
di: Maru, Vatsal
Pubblicazione: (2025)

A Highly Configurable Framework for Large-Scale Thermal Building Data Generation to drive Machine Learning Research
di: Krug, Thomas, et al.
Pubblicazione: (2025)

Benchmarking M-LTSF: Frequency and Noise-Based Evaluation of Multivariate Long Time Series Forecasting Models
di: Janssen, Nick, et al.
Pubblicazione: (2025)

CNN-based Compressor Mass Flow Estimator in Industrial Aircraft Vapor Cycle System
di: Reverdi, Justin, et al.
Pubblicazione: (2024)

Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data
di: Zhang, Thomas T. C. K., et al.
Pubblicazione: (2023)

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data
di: Xin, Lei, et al.
Pubblicazione: (2025)

Heat Death of Generative Models in Closed-Loop Learning
di: Marchi, Matteo, et al.
Pubblicazione: (2024)

Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learning
di: Jonas, Stefan, et al.
Pubblicazione: (2025)

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents
di: Banker, Thomas, et al.
Pubblicazione: (2025)

eVTOL Aircraft Energy Overhead Estimation under Conflict Resolution in High-Density Airspaces
di: Zongo, Alex, et al.
Pubblicazione: (2026)

When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter
di: Li, Yansong, et al.
Pubblicazione: (2024)

Synthetic Data Generation for Minimum-Exposure Navigation in a Time-Varying Environment using Generative AI Models
di: Bapat, Nachiket U., et al.
Pubblicazione: (2025)

Optimal Constant Climb Airspeed with Variable Cost Index for All-electric Aircraft
di: Silva, Lucas Souza e, et al.
Pubblicazione: (2024)

Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models
di: Theiner, Lukas, et al.
Pubblicazione: (2026)

Learning a Class of Mixed Linear Regressions: Global Convergence under General Data Conditions
di: Liu, Yujing, et al.
Pubblicazione: (2025)

EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion Models
di: Lin, Nan, et al.
Pubblicazione: (2024)

Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
di: Zhang, Thomas T., et al.
Pubblicazione: (2024)

Generalizable Machine Learning Models for Predicting Data Center Server Power, Efficiency, and Throughput
di: Lei, Nuoa, et al.
Pubblicazione: (2025)

Learning Dynamical Systems by Leveraging Data from Similar Systems
di: Xin, Lei, et al.
Pubblicazione: (2023)

SmartMeterFM: Unifying Smart Meter Data Generative Tasks Using Flow Matching Models
di: Lin, Nan, et al.
Pubblicazione: (2026)

From Big Data to Fast Data: Towards High-Quality Datasets for Machine Learning Applications from Closed-Loop Data Collection
di: Reis, Philipp, et al.
Pubblicazione: (2026)

Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids
di: Gillioz, Marc, et al.
Pubblicazione: (2026)

Fast Surrogate Models for Adaptive Aircraft Trajectory Prediction in En route Airspace
di: Pepper, Nick, et al.
Pubblicazione: (2026)

Interpretable Physics Extraction from Data for Linear Dynamical Systems using Lie Generator Networks
di: Jamil, Shafayeth, et al.
Pubblicazione: (2026)

Generative Design for Direct-to-Chip Liquid Cooling for Data Centers
di: Liu, Zheng
Pubblicazione: (2026)

Naga: Vedic Encoding for Deep State Space Models
di: Schaller, Melanie, et al.
Pubblicazione: (2025)

Dictionary-Learning-Based Data Pruning for System Identification
di: Wang, Tingna, et al.
Pubblicazione: (2025)

Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?
di: Wu, Xu, et al.
Pubblicazione: (2025)

Learning the Optimal Power Flow: Environment Design Matters
di: Wolgast, Thomas, et al.
Pubblicazione: (2024)

Symmetric Linear Dynamical Systems are Learnable from Few Observations
di: Vu, Minh, et al.
Pubblicazione: (2025)

Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees
di: de Jong, Thomas, et al.
Pubblicazione: (2024)