270 citations · 618 across the 36 of their papers we have counts for
7 papers · 1 filter
Accelerating the estimation of energetic particle confinement statistics in stellarators using multifidelity Monte Carlo
Frederick Law, Antoine Cerfon, Benjamin Peherstorfer
In the design of stellarators, energetic particle confinement is a critical point of concern which remains challenging to study from a numerical point of view. Standard Monte Carlo…
An Extensible Benchmark Suite for Learning to Simulate Physical Systems
Karl Otness, Arvi Gjoka, Joan Bruna +4
Simulating physical systems is a core component of scientific computing, encompassing a wide range of physical domains and applications. Recently, there has been a surge in data-dr…
Active operator inference for learning low-dimensional dynamical-system models from noisy data
Wayne Isaac Tan Uy, Yuepeng Wang, Yuxiao Wen +1
Noise poses a challenge for learning dynamical-system models because already small variations can distort the dynamics described by trajectory data. This work builds on operator in…
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…
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems
Terrence Alsup, Luca Venturi, Benjamin Peherstorfer
This work presents a multilevel variant of Stein variational gradient descent to more efficiently sample from target distributions. The key ingredient is a sequence of distribution…
Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories
Wayne Isaac Tan Uy, Benjamin Peherstorfer
This work introduces a non-intrusive model reduction approach for learning reduced models from partially observed state trajectories of high-dimensional dynamical systems. The prop…
Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis
Julia Konrad, Ionut-Gabriel Farcas, Benjamin Peherstorfer +4
The linear micro-instabilities driving turbulent transport in magnetized fusion plasmas (as well as the respective nonlinear saturation mechanisms) are known to be sensitive with r…