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Maschinelles Lernen
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keywords:("Maschinelles Lernen")
Introduction to the Virtual Collection on Machine Learning in Aerospace
Book Review: Matrix Methods: Applied Linear Algebra and Sabermetrics
Scalable Projection-Based Reduced-Order Models for Large Multiscale Fluid Systems
Mathematics-Informed Neural Network for Acoustic Scattering by Parallel Plates
Fast Buffet-Onset Prediction and Optimization Method Based on Pretrained Flowfield Prediction Model
Data-Driven Nonintrusive Model-Order Reduction for Aerodynamic Design Optimization
Data-Driven Approach Toward Airspace Design for Regional Air Mobility Operations in Korea
Deep Learning Closure of the Navier–Stokes Equations for Transition-Continuum Flows
Precision Mars Entry Navigation with Atmospheric Density Adaptation via Neural Networks
Optimization of Pin Fins Using Computational Fluid Dynamics and Machine Learning
Deep Learning for Real-Time Satellite Pose Estimation on Tensor Processing Units
Data-Driven Model to Predict Aircraft Vibration Environment
Verification of Image-Based Neural Network Controllers Using Generative Models
Machine Learning Approach for Identification of Microstructure–Process Linkages
Monocular Relative Pose Estimation Pipeline for Uncooperative Resident Space Objects
Enabling Limited Resource-Bounded Disjunction in Scheduling
Using Deep Learning to Predict Unstable Approaches for General Aviation Aircraft