Federated learning (FL) is a promising framework for learning from distributed data while
maintaining privacy. The development of efficient FL algorithms encounters various …

In this paper, we study the inverse problem of identifying the parameters in a nonlinear
subdiffusion model from an observation defined in the given Ω1 subset of Ω. The nonlinear …

We study the combination of the alternating direction method of multipliers (ADMM) with
physics-informed neural networks (PINNs) for a general class of nonsmooth partial …

An estimation for the unknown source term in the time-fractional diffusion equation from
measurement data by the alternating direction method of multipliers (ADMM) is considered …

Due to their high performance in modeling and forecasting a large amount of real-world
complex phenomena, deep convolutional neural networks have received a great deal of …

We consider a general class of nonsmooth optimal control problems with partial differential
equation (PDE) constraints, which are very challenging due to its nonsmooth objective …

We consider the problem of identifying a sparse initial source condition to achieve a given
state distribution of a diffusion–advection partial differential equation after a given final time …

We present an Alternating Direction Method of Multipliers (ADMM) algorithm designed to
solve the Weighted Generalized Fused LASSO Signal Approximator (wFLSA). First, we …

This paper focuses on a high-dimensional semi-parametric regression model in which a
partially linear model is used for the parametric part and the B-spline basis function …

In this article, we introduce an advanced approach for enhanced image denoising using an
improved space-variant anisotropic Partial Differential Equation (PDE) framework …