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Physics-based learning for aircraft dynamics simulation
Yang Yu
, Houpu Yao
, Yongming Liu
Architectural Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
26
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Scopus citations
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Dive into the research topics of 'Physics-based learning for aircraft dynamics simulation'. Together they form a unique fingerprint.
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Keyphrases
Dynamic Simulation
100%
Deep Residual
100%
Aircraft Dynamics
100%
Physics Learning
100%
Residual Recurrent Neural Network
100%
Dynamical Systems
50%
Computational Efficiency
50%
Learning Model
50%
Numerical Methods
25%
Physical Model
25%
Control Input
25%
Six Degrees of Freedom
25%
Neural Network
25%
Prediction Accuracy
25%
Computation Cost
25%
Underground Physics
25%
Aircraft Model
25%
Data-driven Learning
25%
Physics Concepts
25%
Flight Trajectory
25%
Air Transportation System
25%
Hybrid Approach
25%
Training Cost
25%
Fourth Order Runge-Kutta Method
25%
Air Traffic System
25%
Simulation Costs
25%
Physics-aware
25%
Controlled Disturbances
25%
Extrapolation Ability
25%
Boeing 747
25%
Earth and Planetary Sciences
Dynamical System
100%
Real Time
50%
Air Traffic
50%
Air Transportation
50%
Mathematical Method
50%
Degree of Freedom
50%
Physics
Physics
100%
Neural Network
83%
Dynamical System
33%
Mathematical Method
16%
Degree of Freedom
16%
Air Traffic
16%
Air Transportation
16%
Engineering
Aircraft
100%
Recurrent Neural Network
44%
Degree of Freedom
11%
Numerical Methods
11%
Control Input
11%
Accurate Prediction
11%
Hybrid Approach
11%
Physical Model
11%
Fourth Order
11%
Aircraft Model
11%
Air Traffic
11%
Air Transportation
11%
Flight Trajectory
11%
Runge-Kutta Method
11%