11 citations · 25 across the 8 of their papers we have counts for
10 papers
A Novel Adaptive Causal Sampling Method for Physics-Informed Neural Networks
Jia Guo, Haifeng Wang, Chenping Hou
Physics-Informed Neural Networks (PINNs) have become a kind of attractive machine learning method for obtaining solutions of partial differential equations (PDEs). Training PINNs c…
Kernel Methods for Regression in Continuous Time over Subsets and Manifolds
Nathan Powell, Jia Guo, Sai Tej Parachuri +3
This paper derives error bounds for regression in continuous time over subsets of certain types of Riemannian manifolds.The regression problem is typically driven by a nonlinear ev…
Strictly Decentralized Adaptive Estimation of External Fields using Reproducing Kernels
Jia Guo, Michael E. Kepler, Sai Tej Paruchuri +3
This paper describes an adaptive method in continuous time for the estimation of external fields by a team of agents. The agents each explore subdomains of a bounded…
Kernel Center Adaptation in the Reproducing Kernel Hilbert Space Embedding Method
Sai Tej Paruchuri, Jia Guo, Andrew Kurdila
The performance of adaptive estimators that employ embedding in reproducing kernel Hilbert spaces (RKHS) depends on the choice of the location of basis kernel centers. Parameter co…
Sufficient Conditions for Parameter Convergence over Embedded Manifolds using Kernel Techniques
Sai Tej Paruchuri, Jia Guo, Andrew Kurdila
The persistence of excitation (PE) condition is sufficient to ensure parameter convergence in adaptive estimation problems. Recent results on adaptive estimation in reproducing ker…
Approximations of the Reproducing Kernel Hilbert Space (RKHS) Embedding Method over Manifolds
Jia Guo, Sai Tej Paruchuri, Andrew J. Kurdila
The reproducing kernel Hilbert space (RKHS) embedding method is a recently introduced estimation approach that seeks to identify the unknown or uncertain function in the governing…