Physics-informed neural networks (PINNs) are effective in solving inverse problems based
on differential and integro-differential equations with sparse, noisy, unstructured, and multi …

The identification of the right order of the equation in applied fractional modeling plays an
important role. In this paper we consider an inverse problem for determining the order of …

A new physics-informed neural network (PINN) algorithm is proposed to solve variable-order
space-fractional partial differential equations (PDEs). For the forward problem, PINN …

An inverse problem for determining the order of the Caputo time-fractional derivative in a
subdiffusion equation with an arbitrary positive self-adjoint operator A with discrete spectrum …

In this work, we investigate the recovery of a parameter in a diffusion process given by the
order of derivation in time for a class of diffusion-type equations, including both classical and …

In this paper, we propose a QSC-L 1 method to solve the two-dimensional variable-order
time-fractional mobile-immobile diffusion (TF-MID) equations with variably diffusive …

In this work we investigate an inverse problem of identifying a spatially variable order in the
one-dimensional subdiffusion model from the boundary flux measurement. The model …

This paper deals with an inverse problem on determining a time dependent potential in a
diffusion equation with temporal fractional derivative of variable order from a distributed …

A fractional wave equation with a fractional Riemann–Liouville derivative is considered. An
arbitrary self-adjoint operator A with a discrete spectrum was taken as the elliptic part. We …

arxiv:2006.15046v1 [math.AP] 26 Jun 2020 Page 1 arxiv:2006.15046v1 [math.AP] 26 Jun
2020 UNIQUENESS IN DETERMINING THE ORDERS OF TIME AND SPATIAL FRACTIONAL …