  Physics Informed Neural Network (PINN) is a scientific computing framework
used to solve both forward and inverse problems modeled by Partial Differential
Equations (PDEs). This paper introduces IDRLnet, a Python toolbox for modeling
and solving problems through PINN systematically. IDRLnet constructs the
framework for a wide range of PINN algorithms and applications. It provides a
structured way to incorporate geometric objects, data sources, artificial
neural networks, loss metrics, and optimizers within Python. Furthermore, it
provides functionality to solve noisy inverse problems, variational
minimization, and integral differential equations. New PINN variants can be
integrated into the framework easily. Source code, tutorials, and documentation
are available at \url{https://github.com/idrl-lab/idrlnet}.
