4 citations · 4 across the 3 of their papers we have counts for
4 papers
A unified perspective of Gaussian process approximation for differential equations
Mengwu Guo
The use of Gaussian processes for approximating differential equations has expanded rapidly, leading to a growing, diverse, and fragmented body of numerical methods. We present a u…
Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference
Shane A. McQuarrie, Mengwu Guo, Anirban Chaudhuri
This work develops an active learning framework to intelligently enrich data-driven reduced-order models (ROMs) of parametric dynamical systems, which can serve as the foundation o…
Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems
Shane A. McQuarrie, Anirban Chaudhuri, Karen E. Willcox +1
This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use…
Gaussian process learning of nonlinear dynamics
Dongwei Ye, Mengwu Guo
One of the pivotal tasks in scientific machine learning is to represent underlying dynamical systems from time series data. Many methods for such dynamics learning explicitly requi…