fastsurrogates

fastsurrogates is a Python library designed for efficient surrogate modeling, particularly in the context of scientific simulations and engineering applications. Surrogates, also known as response surfaces or metamodels, are simplified mathematical models that approximate the behavior of a more complex, computationally expensive model. This approximation allows for faster exploration of design spaces, uncertainty quantification, and optimization.

The library focuses on speed and ease of use, offering various surrogate model types. These include Gaussian Processes, polynomial chaos expansions, and radial basis functions. Each type has its strengths and is suited for different problem characteristics, such as the dimensionality of the input space and the smoothness of the underlying function. fastsurrogates provides tools for fitting these models to data generated from the original complex model and then using the fitted surrogate for predictions.

Key features of fastsurrogates include its ability to handle high-dimensional input spaces and its integration with common scientific computing libraries like NumPy and SciPy. The library aims to democratize the use of surrogate modeling by simplifying the implementation and deployment process for researchers and engineers. This makes it a valuable tool for accelerating the analysis and design of systems where direct simulation is prohibitive.