AI/ML and HPC Applications

Modern scientific workflows increasingly include AI/ML components for pattern recognition, anomaly detection, and surrogate modeling. The orchestration layer must therefore support the full AI/ML lifecycle, including distributed training across HPC/Cloud, federated learning where data cannot leave sites, inference deployment at the edge for low-latency decisions, and model versioning for reproducibility. In both HEP and RA, this could mean deploying pre-trained models at instrument edges to filter events in real time. In HEP, it could also enable large-scale distributed training of generative models or event reconstruction models on exascale resources.

Advanced Analytics and Interactive Exploration

Researchers need tools to explore and analyze large datasets. The continuum offers platforms such as Jupyter notebooks, RStudio, or custom portals linked to the data federation, allowing researchers to query and visualize large datasets through notebooks, dashboards, and statistical tools, bridging traditional batch workflows with real-time exploration. These interactive environments run in the cloud or on HPC visualization nodes, giving scientists graphical and scripting access to data. Statistical and visual analytics libraries (Python, R, machine learning toolkits) are preinstalled. This lowers the barrier for hypothesis generation. Large scale data analysis can start as an interactive process, used to test ideas and solutions on a small-to-medium scale, but can then process as an offline workflow, particularly in cases where the input datasets are PB scale. A seamless transition between the two regimes should be possible, for example via the scale-out of interactive processes from the processing on the user’s private machine to large external resources, via tools like Dask.

AI/ML Platforms, Distributed Model Training and Hyperparameter Optimization

AI and ML are becoming indispensable in modern scientific discovery. The continuum provides AI/ML platforms with the tools, infrastructure, and services needed to build, train, deploy, and manage models tailored for large-scale, distributed research. Training infrastructure makes available distributed, GPU-rich training environments spanning HPC, cloud, and edge resources thus enabling models to be trained on massive datasets, including exabyte-scale HEP events or petabyte-scale RA visibility data. Scalable inference services enable deployment of trained models, e.g., at the LHC trigger level to classify collision events, or at telescope edges to detect astrophysical transients within milliseconds. Model registries and versioning play a crucial role. Both AI/ML and HPC workflows benefit from robust software, model, and dataset registries. FAIR-compliant repositories facilitate reproducibility, attribution, and long-term preservation and support collaborative development across geographically distributed communities. For example, an astrophysicist can browse a model zoo for a neural network that classifies radio transients and fine-tune it with local data. Federated learning tools provide privacy-preserving approaches that allow AI models to be collaboratively trained across distributed data without moving sensitive or bandwidth-heavy datasets, an approach particularly relevant for geographically dispersed observatories.

Simulation Frameworks and Digital Twins

Many scientific domains require physics simulations. The continuum hosts domain-specific simulation frameworks (e.g. GEANT4 for particle detectors, N-body or fluid solvers for astrophysics). An emerging class of applications builds on digital twins, which combine physics models, operational data, and AI-assisted prediction to represent instruments or detectors in near real time. Digital twin platforms, such as itwinai, allow domain experts to exploit advanced optimisation or multi-node training methods without extensive ML engineering. For RA, digital twins of telescope arrays could for example anticipate calibration drifts or hardware failures while for HEP, digital twins of detectors could for instance model radiation damage or alignment shifts.

Large-Scale Data Processing and Scientific Pipelines

Many HEP and RA applications rely on large-scale, domain-specific processing pipelines that transform raw instrument output into calibrated, analysis-ready data. These include full reconstruction chains, detector calibration workflows, Monte Carlo production, data reduction for RA, and large-scale physics or astrophysics analyses. These pipelines often process petabyte-scale datasets, require sustained compute allocations, and combine CPU- and accelerator-intensive components. Within the continuum, these pipelines must run efficiently on heterogeneous resources and access data through federated mechanisms without requiring per-site customisation. Containerised software environments, standardised data access protocols, and compatibility with heterogeneous architectures are essential to ensuring that these large-scale applications can utilise HPC, HTC, and cloud resources as part of an integrated ecosystem.