continuiti: Learning Function Operators with Neural Networks

continuiti is a Python package for machine learning on function operators. It implements various neural operator architectures (e.g., DeepONets), physics-informed loss functions, and a collection of examples and benchmarks.

Function operators are ubiquitous in mathematics and physics: They are used to describe dynamics of physical systems, such as the Navier-Stokes equations in fluid dynamics. As solutions of these systems are functions, it is natural to transfer the concept of function mapping into machine learning.

Checkout the Documentation page to learn more about operator learning in continuiti. If you prefer to jump into code directly, take a look at our collection of Examples.

continuiti implements the following neural operator architectures:

To see what new methods, features and improvements are coming, check out the issues on GitHub.


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