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Found 7 papers, 0 papers with code
Learning Memory Kernels in Generalized Langevin Equations
We introduce a novel approach for learning memory kernels in Generalized Langevin Equations.
Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel
Modeling multi-agent systems on networks is a fundamental challenge in a wide variety of disciplines.
Small noise analysis for Tikhonov and RKHS regularizations
We establish a small noise analysis framework to assess the effects of norms in Tikhonov and RKHS regularizations, in the context of ill-posed linear inverse problems with Gaussian noise.
A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators
However, a fixed non-degenerate prior leads to a divergent posterior mean when the observation noise becomes small, if the data induces a perturbation in the eigenspace of zero eigenvalues of the inversion operator.
Data adaptive RKHS Tikhonov regularization for learning kernels in operators
We present DARTR: a Data Adaptive RKHS Tikhonov Regularization method for the linear inverse problem of nonparametric learning of function parameters in operators.
Identifiability of interaction kernels in mean-field equations of interacting particles
This study examines the identifiability of interaction kernels in mean-field equations of interacting particles or agents, an area of growing interest across various scientific and engineering fields.
Learning interaction kernels in mean-field equations of 1st-order systems of interacting particles
We introduce a nonparametric algorithm to learn interaction kernels of mean-field equations for 1st-order systems of interacting particles.
