Paper

Iterative parallel-in-time algorithms like Parareal can extend scaling beyond the saturation of purely spatial parallelization when solving initial value problems. However, they require the user to build coarse models to handle the unavoidable serial transport of information in time. This is a time-consuming and difficult process since there is still limited theoretical insight into what constitutes a good and efficient coarse model. Novel approaches from machine learning to solve differential equations could provide a more generic way to find coarse-level models for parallel-in-time algorithms.
This paper demonstrates that a physics-informed Fourier Neural Operator (PINO) is an effective coarse model for the parallelization in time of the two-asset Black-Scholes equation using Parareal. We demonstrate that PINO-Parareal converges as fast as a bespoke numerical coarse model and that, in combination with spatial parallelization by domain decomposition, it provides better overall speedup than both purely spatial parallelization and space-time parallelization with a numerical coarse propagator.

The iMagine platform leverages AI-driven tools to enhance the analysis of imaging data in marine and freshwater research, contributing to the study of ocean, sea, coastal, and inland water health. Connected to the European Open Science Cloud (EOSC), it enables the development, training, and deployment of AI models, collaborating with twelve aquatic science use cases to provide valuable insights. The platform refines existing solutions from data acquisition and preprocessing to provide trained models as a service for users. iMagine outlines various AI-based tools, techniques, and methodologies for aquatic science image processing, ensuring consistency and accuracy through clear annotation guidelines and verified tools. The preparation of training datasets, along with their metadata, ensures FAIRness and effective publishing in data repositories. Deep learning models, such as convolutional neural networks, are used for classification, object detection, and segmentation, with performance metrics and evaluation tools ensuring reproducibility and transparency. AI model drift and data FAIRness are also explored, alongside case studies on AI challenges in aquatic sciences. By implementing these practices, iMagine enhances data quality, promotes reproducibility, and fosters scientific progress in aquatic research while collaborating with projects like AI4EOSC and Blue-Cloud. The platform allows users to develop, train, share, and serve AI models on its marketplace. The AI models are encapsulated as Docker images and integrated with REST APIs to ensure their reproducibility. Researchers benefit from the platform's flexibility, which enables seamless execution of these Docker containers on both federated clouds of the European Grid Infrastructure (EGI) and High-Performance Computing (HPC) infrastructures.