Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

Mathematics

• Model Reduction 100%
• Dynamical system 62%
• Subspace 59%
• Reduced Order Model 47%
• Advection 27%
• Discrete-time 19%
• Reduced Basis Methods 18%
• Balanced Truncation 17%
• Projection 17%
• Proper Orthogonal Decomposition 16%
• Rational Interpolation 15%
• Petrov-Galerkin 15%
• High Accuracy 12%
• Benchmark 11%
• Galerkin 11%
• Governing equation 11%
• Learning 10%
• Error Bounds 10%
• Least Squares 10%
• Continuous Time 10%
• State Space 10%
• Formulation 7%
• Computing 7%
• Relationships 7%
• Demonstrate 7%
• Framework 6%
• Model 3%

Physics & Astronomy

• dynamical systems 83%
• advection 38%
• projection 26%
• learning 18%
• interpolation 16%
• decomposition 12%
• formulations 11%
• approximation 8%

Engineering & Materials Science

• Dynamical systems 68%
• Advection 42%
• Nonlinear dynamical systems 22%
• ROM 20%
• Deep learning 15%
• Interpolation 14%
• Decomposition 12%