13 citations · 25 across the 3 of their papers we have counts for
5 papers · 1 filter
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
Peter Benner, Pawan Goyal, Boris Kramer +2
This work presents a non-intrusive model reduction method to learn low-dimensional models of dynamical systems with non-polynomial nonlinear terms that are spatially local and that…
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
Elizabeth Qian, Boris Kramer, Benjamin Peherstorfer +1
We present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system's governing equa…
Balanced Truncation Model Reduction for Lifted Nonlinear Systems
Boris Kramer, Karen E. Willcox
We present a balanced truncation model reduction approach for a class of nonlinear systems with time-varying and uncertain inputs. First, our approach brings the nonlinear system i…
Multifidelity Dimension Reduction via Active Subspaces
Rémi Lam, Olivier Zahm, Youssef Marzouk +1
We propose a multifidelity dimension reduction method to identify a low-dimensional structure present in many engineering models. The structure of interest arises when functions va…
Survey of multifidelity methods in uncertainty propagation, inference, and optimization
Benjamin Peherstorfer, Karen Willcox, Max Gunzburger
In many situations across computational science and engineering, multiple computational models are available that describe a system of interest. These different models have varying…