Welcome to idrlnet’s documentation!¶
Features¶
IDRLnet is a machine learning library on top of PyTorch. Use IDRLnet if you need a machine learning library that solves both forward and inverse differential equations via physics-informed neural networks (PINN). IDRLnet is a flexible framework inspired by Nvidia Simnet.
IDRLnet supports
complex domain geometries without mesh generation. Provided geometries include interval, triangle, rectangle, polygon, circle, sphere… Other geometries can be constructed using three boolean operations: union, difference, and intersection;
sampling in the interior of the defined geometry or on the boundary with given conditions.
enables the user code to be structured. Data sources, operations, constraints are all represented by
Node. The graph will be automatically constructed via label symbols of each node. Getting rid of the explicit construction via explicit expressions, users model problems more naturally.solving variational minimization problem;
solving integral differential equation;
adaptive resampling;
recover unknown parameter of PDEs from noisy measurement data.
API reference¶
If you are looking for usage of a specific function, class or method, please refer to the following part.