Projection-based model reduction of dynamical systems using space–time subspace and machine learning

Mathematics

• Model Reduction 93%
• Machine Learning 86%
• Space-time 67%
• Dynamical system 58%
• Projection 58%
• Subspace 55%
• Engineering 48%
• Heat Conduction 41%
• Surrogate Model 30%
• Random Forest 28%
• Tensor Decomposition 28%
• Snapshot 27%
• K-nearest Neighbor 26%
• Polynomial Regression 25%
• Temperature Field 24%
• Multivariate Regression 23%
• Physical Model 23%
• Multivariate Polynomials 21%
• Training 20%
• Parametric Model 20%
• Computational Cost 20%
• Parametrization 19%
• Neural Networks 18%
• Predict 18%
• High-dimensional 16%
• Knowledge 15%
• Output 14%
• Methodology 14%
• Costs 14%
• Numerical Results 13%
• Computing 12%
• Demonstrate 11%
• Coefficient 9%
• Model 6%

Physics & Astronomy

• machine learning 100%
• dynamical systems 78%
• projection 62%
• engineering 43%
• conductive heat transfer 38%
• costs 29%
• regression analysis 20%
• pressure distribution 19%
• education 19%
• polynomials 17%
• temperature distribution 15%
• methodology 15%
• decomposition 14%
• tensors 14%
• expansion 12%
• output 11%
• coefficients 10%

Engineering & Materials Science

• Dynamical systems 64%
• Machine learning 57%
• Heat conduction 40%
• Polynomial regression 27%
• Random forests 20%
• Tensors 19%
• Temperature distribution 16%
• Costs 14%
• Decomposition 14%
• Neural networks 11%