1 citations · 1 across the 2 of their papers we have counts for
4 papers · 1 filter
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference
Nihar Sawant, Boris Kramer, Benjamin Peherstorfer
Operator inference learns low-dimensional dynamical-system models with polynomial nonlinear terms from trajectories of high-dimensional physical systems (non-intrusive model reduct…
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…