PINNs | Physics Informed Deep Learning : Data-driven Solutions | Machine Learning library

PINNs is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. PINNs has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However PINNs build file is not available. You can download it from GitHub.

We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. We present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct classes of algorithms, namely continuous time and discrete time models. The resulting neural networks form a new class of data-efficient universal function approximators that naturally encode any underlying physical laws as prior information. In the first part, we demonstrate how these networks can be used to infer solutions to partial differential equations, and obtain physics-informed surrogate models that are fully differentiable with respect to all input coordinates and free parameters. In the second part, we focus on the problem of data-driven discovery of partial differential equations. For more information, please refer to the following: (