- Efficient Sampling Methods for Machine Learning Error Models with application to Surrogates of Steady Hypersonic Flows
Elizabeth H. Krath, David S. Ching and Patrick J. Blonigan AIAA 2022-1249 This paper presents an investigation into sampling strategies for reducing the computational expense of creating error models for steady hypersonic flow surrogate models. The error model describes the quantity of interest error between a reduced-order model prediction and a full-order model solution. The sampling strategies are […]
- A Compute-Bound Formulation of Galerkin Model Reduction for Linear Time-Invariant Dynamical Systems
Francesco Rizzi, Eric Parish, Patrick Blonigan, and John Tencer Computer Methods in Applied Mechanics and Engineering, 384(1) This work aims to advance computational methods for projection-based reduced order models (ROMs) of linear time-invariant (LTI) dynamical systems. For such systems, current practice relies on ROM formulations expressing the state as a rank-1 tensor (i.e., a vector), […]
- Projection-Based Model Reduction for Coupled Conduction-Enclosure Radiation Systems
Victor E Brunini, Eric Parish, John Tencer, and Francesco Rizzi ASME Journal of Heat Transfer A projection-based reduced order model (pROM) methodology has been developed for transient heat transfer problems involving coupled conduction and enclosure radiation. The approach was demonstrated on two test problems of varying complexity. The reduced order models demonstrated substantial speedups (up […]
- A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data using Unstructured Spatial Discretizations
J. Tencer and K. Potter SIAM J. Sci. Comput., 43(4), A2581–A2613 We propose a nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries. Convolutional neural networks have proven to be highly advantageous for compressing data arising from systems demonstrating a slow-decaying Kolmogorov n-width. […]
Francesco Rizzi, Eric Parish, Patrick Blonigan, and John Tencer Computer Methods in Applied Mechanics and Engineering, 384(1) This work aims to advance computational methods for projection-based reduced order models (ROMs) of linear time-invariant (LTI) dynamical systems. For such systems, current practice relies on ROM formulations expressing the state as a rank-1 tensor (i.e., a vector), […]
Victor E Brunini, Eric Parish, John Tencer, and Francesco Rizzi ASME Journal of Heat Transfer A projection-based reduced order model (pROM) methodology has been developed for transient heat transfer problems involving coupled conduction and enclosure radiation. The approach was demonstrated on two test problems of varying complexity. The reduced order models demonstrated substantial speedups (up […]
J. Tencer and K. Potter SIAM J. Sci. Comput., 43(4), A2581–A2613 We propose a nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries. Convolutional neural networks have proven to be highly advantageous for compressing data arising from systems demonstrating a slow-decaying Kolmogorov n-width. […]