270 citations · 618 across the 36 of their papers we have counts for
5 papers · 1 filter
Manifold Approximations via Transported Subspaces: Model reduction for transport-dominated problems
Donsub Rim, Benjamin Peherstorfer, Kyle T. Mandli
This work presents a method for constructing online-efficient reduced models of large-scale systems governed by parametrized nonlinear scalar conservation laws. The solution manifo…
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 low-dimensional dynamical-system models from noisy frequency-response data with Loewner rational interpolation
Zlatko Drmač, Benjamin Peherstorfer
Loewner rational interpolation provides a versatile tool to learn low-dimensional dynamical-system models from frequency-response measurements. This work investigates the robustnes…
Sampling low-dimensional Markovian dynamics for pre-asymptotically recovering reduced models from data with operator inference
Benjamin Peherstorfer
This work introduces a method for learning low-dimensional models from data of high-dimensional black-box dynamical systems. The novelty is that the learned models are exactly the…
Multifidelity probability estimation via fusion of estimators
Boris Kramer, Alexandre Noll Marques, Benjamin Peherstorfer +2
This paper develops a multifidelity method that enables estimation of failure probabilities for expensive-to-evaluate models via information fusion and importance sampling. The pre…