This work proposes a conservative manifold least-squares Petrov–Galerkin (LSPG) projection approach for model reduction of steady hypersonic aerodynamics. Hypersonic flows exhibit complex physics including shock waves, boundary layers, and …
This work proposes a windowed least-squares approach for model reduction of dynamical systems. The method constructs reduced-order models by minimizing the residual over a sliding time window, which enables the method to adapt to the local dynamics …
This work proposes an approach for latent dynamics learning that exactly enforces physical conservation laws. The method comprises two steps. First, we compute a low-dimensional embedding of the high-dimensional dynamical-system state using deep …
In many applications, projection-based reduced-order models (ROMs) have demonstrated the ability to provide rapid approximate solutions to high-fidelity full-order models (FOMs). However, there is no a priori assurance that these approximate …
This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical systems. In particular, we extend the machine-learning error models (MLEM) framework proposed in [Freno, Carlberg, …
We introduce Pressio, a library for enabling projection-based model reduction for large-scale nonlinear dynamical systems. Pressio provides a non-intrusive wrapper that enables state-of-the-art nonlinear model reduction methods to be seamlessly …
High-speed aerospace engineering applications rely heavily on computational fluid dynamics (CFD) models for design and analysis due to the expense and difficulty of flight tests and experiments. This reliance on CFD models necessitates performing …
Nearly all model-reduction techniques project the governing equations onto a linear subspace of the original state space. Such subspaces are typically computed using methods such as balanced truncation, rational interpolation, the reduced-basis …
This work proposes a windowed least-squares (WLS) approach for model-reduction of dynamical systems. The proposed approach sequentially minimizes the time-continuous full-order-model residual within a low-dimensional space-time trial subspace over …
This work introduces a new method to efficiently solve optimization problems constrained by partial differential equations (PDEs) with uncertain coefficients. The method leverages two sources of inexactness that trade accuracy for speed: (1) …