7 citations · 8 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
Learning physics-based reduced-order models for a single-injector combustion process
Renee Swischuk, Boris Kramer, Cheng Huang +1
This paper presents a physics-based data-driven method to learn predictive reduced-order models (ROMs) from high-fidelity simulations, and illustrates it in the challenging context…
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…