270 citations · 947 across the 25 of their papers we have counts for
11 papers · 1 filter
Global sensitivity analysis with limited data via sparsity-promoting D-MORPH regression: Application to char combustion
Dongjin Lee, Elle Lavichant, Boris Kramer
In uncertainty quantification, variance-based global sensitivity analysis quantitatively determines the effect of each input random variable on the output by partitioning the total…
Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds
Harsh Sharma, Hongliang Mu, Patrick Buchfink +3
This work presents two novel approaches for the symplectic model reduction of high-dimensional Hamiltonian systems using data-driven quadratic manifolds. Classical symplectic model…
Multifidelity conditional value-at-risk estimation by dimensionally decomposed generalized polynomial chaos-Kriging
Dongjin Lee, Boris Kramer
We propose novel methods for Conditional Value-at-Risk (CVaR) estimation for nonlinear systems under high-dimensional dependent random inputs. We develop a novel DD-GPCE-Kriging su…
Bi-fidelity conditional value-at-risk estimation by dimensionally decomposed generalized polynomial chaos expansion
Dongjin Lee, Boris Kramer
Digital twin models allow us to continuously assess the possible risk of damage and failure of a complex system. Yet high-fidelity digital twin models can be computationally expens…
Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale dynamical systems
Harsh Sharma, Boris Kramer
This work presents a nonintrusive physics-preserving method to learn reduced-order models (ROMs) of Lagrangian systems, which includes nonlinear wave equations. Existing intrusive…
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…
Hamiltonian Operator Inference: Physics-preserving Learning of Reduced-order Models for Canonical Hamiltonian Systems
Harsh Sharma, Zhu Wang, Boris Kramer
This work presents a nonintrusive physics-preserving method to learn reduced-order models (ROMs) of canonical Hamiltonian systems. Traditional intrusive projection-based model redu…
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…
Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition
Boris Kramer, Karen Willcox
This paper presents a structure-exploiting nonlinear model reduction method for systems with general nonlinearities. First, the nonlinear model is lifted to a model with more struc…