Abstract

In the last decade, machine learning (ML) and deep learning (DL) techniques have revolutionised many fields within science, industry, and beyond. Researchers across domains are increasingly seeking to combine ML with numerical modelling to advance research. This typically brings about the challenge of programming language interoperation. PyTorch (Paszke et al., 2019) is a popular framework for designing and training ML/DL models whilst Fortran remains a language of choice for many high-performance computing (HPC) scientific models.

The FTorch library provides an easy-to-use, performant, cross-platform method for coupling the two, allowing users to call PyTorch models from Fortran. This talk will introduce the challenges and our approach to solving them. We will look at building an efficient but friendly piece of software for researchers, touch on recent developments including hardware acceleration and automatic differentiation capabilities, and take a look at some recent applications of the software in hybrid modelling.

Reference: Atkinson et al., (2025). FTorch: a library for coupling PyTorch models to Fortran. Journal of Open Source Software, 10(107), 7602 https://doi.org/10.21105/joss.07602