8 papers
Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networks
Tomoki Koike, Prakash Mohan, Marc T. Henry de Frahan +2
Linear dimensionality reduction methods such as proper orthogonal decomposition (POD) make high-dimensional data amenable to analysis by identifying the principal components, or mo…
Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data
Atticus Rex, Elizabeth Qian, David Peterson
Supervised machine learning describes the practice of fitting a parameterized model to labeled input-output data. Supervised machine learning methods have demonstrated promise in l…
Likelihood-informed Model Reduction for Bayesian Inference of Static Structural Loads
Jakob Scheffels, Elizabeth Qian, Iason Papaioannou +1
Bayesian inverse problems use data to update a prior probability distribution on uncertain parameter values to a posterior distribution. Such problems arise in many structural engi…
Dimension and model reduction approaches for linear Bayesian inverse problems with rank-deficient prior covariances
Josie König, Elizabeth Qian, Melina A. Freitag
Bayesian inverse problems use observed data to update a prior probability distribution for an unknown state or parameter of a scientific system to a posterior distribution conditio…
Streaming Operator Inference for Model Reduction of Large-Scale Dynamical Systems
Tomoki Koike, Prakash Mohan, Marc T. Henry de Frahan +2
Projection-based model reduction enables efficient simulation of complex dynamical systems by constructing low-dimensional surrogate models from high-dimensional data. The Operator…
Physics-Informed Machine Learning for Characterizing System Stability
Tomoki Koike, Elizabeth Qian
In the design and operation of complex dynamical systems, it is essential to ensure that all state trajectories of the dynamical system converge to a desired equilibrium within a g…