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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
Deep Learning Closure of the Navier–Stokes Equations for Transition-Continuum Flows
Data-Driven Approach Toward Airspace Design for Regional Air Mobility Operations in Korea
Optimization of Pin Fins Using Computational Fluid Dynamics and Machine Learning
Precision Mars Entry Navigation with Atmospheric Density Adaptation via Neural Networks
Data-Driven Model to Predict Aircraft Vibration Environment
Deep Learning for Real-Time Satellite Pose Estimation on Tensor Processing Units
Monocular Relative Pose Estimation Pipeline for Uncooperative Resident Space Objects
Machine Learning Approach for Identification of Microstructure–Process Linkages
Machine Learning Reinforced Crystal Plasticity Modeling Under Experimental Uncertainty
Using Deep Learning to Predict Unstable Approaches for General Aviation Aircraft