1.9k citations · 6.2k across the 179 of their papers we have counts for
4 papers · 1 filter
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion
Seungjoon Lee, Felix Dietrich, George E. Karniadakis +1
In statistical modeling with Gaussian Process regression, it has been shown that combining (few) high-fidelity data with (many) low-fidelity data can enhance prediction accuracy, c…
Some manifold learning considerations towards explicit model predictive control
Robert J. Lovelett, Felix Dietrich, Seungjoon Lee +1
Model predictive control (MPC) is a de facto standard control algorithm across the process industries. There remain, however, applications where MPC is impractical because an optim…
Manifold Learning for Organizing Unstructured Sets of Process Observations
Felix Dietrich, Mahdi Kooshkbaghi, Erik M. Bollt +1
Data mining is routinely used to organize ensembles of short temporal observations so as to reconstruct useful, low-dimensional realizations of an underlying dynamical system. In t…
Manifold learning for parameter reduction
Alexander Holiday, Mahdi Kooshkbaghi, Juan M. Bello-Rivas +3
Large scale dynamical systems (e.g. many nonlinear coupled differential equations) can often be summarized in terms of only a few state variables (a few equations), a trait that re…