# AI-coupled HPC Workflows

arXiv: https://arxiv.org/abs/2208.11745 · HTML: https://mlsanity.com/paper/2208.11745/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 13 of 13)

- [Fourier Neural Operator for Parametric Partial Differential Equations](https://mlsanity.com/paper/2010.08895/) (2020)
- [Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data](https://mlsanity.com/paper/1612.08544/) (2016)
- [DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics](https://mlsanity.com/paper/1712.03641/) (2017)
- [Building high accuracy emulators for scientific simulations with deep neural architecture search](https://mlsanity.com/paper/2001.08055/) (2020)
- [DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding](https://mlsanity.com/paper/1909.07817/) (2019)
- [Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research](https://mlsanity.com/paper/1909.00311/) (2019)
- [Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing](https://mlsanity.com/paper/2110.02827/) (2021)
- [Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations](https://mlsanity.com/paper/1909.13340/) (2019)
- [Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation](https://mlsanity.com/paper/1902.10810/) (2019)
- [IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads](https://mlsanity.com/paper/2010.06574/) (2020)
- [Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release](https://mlsanity.com/paper/2006.02431/) (2020)
- [Understanding ML driven HPC: Applications and Infrastructure](https://mlsanity.com/paper/1909.02363/) (2019)
- [Scalable HPC and AI Infrastructure for COVID-19 Therapeutics](https://mlsanity.com/paper/2010.10517/) (2020)

## Built on by (most cited first, 2 of 2)

- [Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources](https://mlsanity.com/paper/2303.08803/) (2023)
- [Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applications](https://mlsanity.com/paper/2503.13343/) (2025)

## Abstract (arXiv)

Increasingly, scientific discovery requires sophisticated and scalable workflows. Workflows have become the ``new applications,'' wherein multi-scale computing campaigns comprise multiple and heterogeneous executable tasks. In particular, the introduction of AI/ML models into the traditional HPC workflows has been an enabler of highly accurate modeling, typically reducing computational needs compared to traditional methods. This chapter discusses various modes of integrating AI/ML models to HPC computations, resulting in diverse types of AI-coupled HPC workflows. The increasing need of coupling AI/ML and HPC across scientific domains is motivated, and then exemplified by a number of production-grade use cases for each mode. We additionally discuss the primary challenges of extreme-scale AI-coupled HPC campaigns -- task heterogeneity, adaptivity, performance -- and several framework and middleware solutions which aim to address them. While both HPC workflow and AI/ML computing paradigms are independently effective, we highlight how their integration, and ultimate convergence, is leading to significant improvements in scientific performance across a range of domains, ultimately resulting in scientific explorations otherwise unattainable.