Data adaptive RKHS Tikhonov regularization for learning kernels in operators
This work addresses the linear inverse problem for learning kernels in operators, with potential applications in integral, nonlinear, and nonlocal operators, but it appears incremental as it builds on existing regularization techniques with a novel adaptation.
The authors tackled the problem of nonparametric learning of function parameters in operators by introducing DARTR, a data-adaptive RKHS Tikhonov regularization method, which resulted in an accurate and robust estimator that converges consistently as data refines and outperforms baseline regularizers.
We present DARTR: a Data Adaptive RKHS Tikhonov Regularization method for the linear inverse problem of nonparametric learning of function parameters in operators. A key ingredient is a system intrinsic data-adaptive (SIDA) RKHS, whose norm restricts the learning to take place in the function space of identifiability. DARTR utilizes this norm and selects the regularization parameter by the L-curve method. We illustrate its performance in examples including integral operators, nonlinear operators and nonlocal operators with discrete synthetic data. Numerical results show that DARTR leads to an accurate estimator robust to both numerical error due to discrete data and noise in data, and the estimator converges at a consistent rate as the data mesh refines under different levels of noises, outperforming two baseline regularizers using $l^2$ and $L^2$ norms.