102 citations · 787 across the 61 of their papers we have counts for
16 papers · 1 filter
GP-HMAT: Scalable, Gaussian Process Regression with Hierarchical Low-Rank Matrices
Vahid Keshavarzzadeh, Shandian Zhe, Robert M. Kirby +1
A Gaussian process (GP) is a powerful and widely used regression technique. The main building block of a GP regression is the covariance kernel, which characterizes the relationshi…
Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference
Elizabeth Qian, Jemima M. Tabeart, Christopher Beattie +4
We consider the Bayesian approach to the linear Gaussian inference problem of inferring the initial condition of a linear dynamical system from noisy output measurements taken afte…
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs
Michael Penwarden, Shandian Zhe, Akil Narayan +1
Physics-informed neural networks (PINNs) as a means of discretizing partial differential equations (PDEs) are garnering much attention in the Computational Science and Engineering…
Meta-Learning with Adjoint Methods
Shibo Li, Zheng Wang, Akil Narayan +2
Model Agnostic Meta Learning (MAML) is widely used to find a good initialization for a family of tasks. Despite its success, a critical challenge in MAML is to calculate the gradie…
Non-Dissipative and Structure-Preserving Emulators via Spherical Optimization
Dihan Dai, Yekaterina Epshteyn, Akil Narayan
Approximating a function with a finite series, e.g., involving polynomials or trigonometric functions, is a critical tool in computing and data analysis. The construction of such a…
Structure-preserving Nonlinear Filtering for Continuous and Discontinuous Galerkin Spectral/hp Element Methods
Vidhi Zala, Robert M. Kirby, Akil Narayan
Finite element simulations have been used to solve various partial differential equations (PDEs) that model physical, chemical, and biological phenomena. The resulting discretized…