June 21, 2023 — PRACE has announced that the 2023 edition of the PRACE HPC Excellence Award has been awarded to Professor Petros Koumoutsakos, the Herbert S. Winokur Jr. Professor for Computing in Science and Engineering at Harvard University.

Prof. Koumoutsakos is awarded for his seminal contributions in the area of high performance computing and his foundational work on the development of advanced modeling techniques coupled with innovations in Artificial Intelligence (AI) and machine learning. The Award will be presented to him at PASC 2023, the Platform for Advanced Scientific Computing Conference, hosted this year in Davos, Switzerland, from 26 to 28 June. Prof. Koumoutsakos will, at this occasion, give a keynote address entitled “AI, Computing and Thinking: Algorithmic Alloys for Advancing Scientific Discovery.”

Prof. Koumoutsakos has produced, throughout his career, reference quality simulations for in fluid mechanics including unsteady separated flows, biological flows and nanofluidics. In addition to simulations, a notable aspect of his work is state of the art flow control and optimization, through novel AI algorithms including stochastic optimization, neural networks, Bayesian inference and, more recently, reinforcement learning (RL).

Prof. Koumoutsakos pioneered the use of Artificial Intelligence (AI) in Fluid Mechanics at the Center of Turbulence Research at Stanford University in the early 90’s where he introduced deep neural to reconstruct the flow field of a turbulent flow using wall only information. He worked in the field of AI and Digital Twins well before they became as popular as they are today. With his students and collaborators he made foundational work on developing stochastic optimization, Bayesian inference and their multiple interfaces with learning algorithms. His group has been heavily involved in HPC, not only in the simulation of large-scale fluid mechanics systems across scales but also more recently in creating potent, open source, scalable software (KORALI) for the deployment of Machine Learning (ML) algorithms as surrogates for Bayesian inference in Uncertainty Quantification.

Among Prof. Koumoutsakos’ ground-breaking works are his contributions over the last five years towards coupling advanced machine learning with state-of-the-art scientific computing for the forecasting of complex systems, the development of closure models for coarse grained models of Partial Differential Equations and learning of effective dynamics of complex multiscale processes. He introduced the scientific Multi-Agent Deep Reinforcement Learning (sciMAD-RL) algorithm that allows for computational elements to serve a dual role as discretization points of governing equations and as learning agents for closures and error correction. He has showcased the advantage of sciMAD-RL methods over supervised learning algorithms in challenging turbulent flows. This automated discovery of closure models was shown to generalise across grid sizes and previously unseen geometries using limited data and predict flows at unprecedented Reynolds numbers. These findings have introduced exciting prospects for turbulence modeling, spanning from applications in aerodynamics to climate modeling.

Prof. Koumoutsakos is awarded for the research presented, in particular, in the following papers:

• G. Novati, H. L. de Laroussilhe, and P. Koumoutsakos, “Automating turbulence modelling by multi-agent reinforcement learning,” Nat. Mach. Intell., 2021
• J. H. Bae and P. Koumoutsakos, “Scientific multi-agent reinforcement learning for wall-models of turbulent flows,” Nat. Commun., vol. 13, iss. 1, 2022
• P. R. Vlachas, G. Arampatzis, C. Uhler, and P. Koumoutsakos, “Multiscale simulations of complex systems by learning their effective dynamics,” Nat. Mach. Intell., 2022.

Efficient use of large HPC facilities and resources is central in Prof. Koumoutsakos work as for most scientists active in fluid mechanics simulation. The method he developed is particularly suited for HPC architectures as it provides a dual role to computational elements as learning agents and discretization points. This increases the computational load for each element in a deterministic (due to discretization) and a stochastic (due to learning) fashion. Also these algorithmic developments are generalizable beyond fluid flow towards advancing simulation capabilities for complex systems.

Prof. Koumoutsakos commented, “I am deeply grateful to PRACE and the entire HPC community, not only for this incredible honour, but also for decades of amazing, sustained support of the research in my group. Throughout my career, I have been blessed with exceptional students and collaborators and this award belongs to each and every one of them.”

Laura Grigori, chair of the HPC Excellence Award Selection Committee, stated, “Petros Koumoutsakos’s nomination to the Award was amongst a series of other excellent candidates. The Committee was impressed by the quality and broad impact of Prof. Koumoutsakos’s work and acknowledges his major contribution in improving methodology and algorithms in addition to pure HPC performance.”

About the PRACE HPC Excellence Award

Awarded for the first time in 2022, the PRACE HPC Excellence Award recognises an outstanding individual or team for ground-breaking research, through the use of high-performance computing, that leads to significant advances in any research field. The award can be given to any individual or team that demonstrates that the work has been communicated and peer-reviewed within five years preceding the nomination deadline.

The Prize Committee, composed of six well-renowned international scientists, evaluate the nominations from the point of view of their outstanding contribution to addressing challenges in all fields of science and engineering – particularly those enabled by innovative methodological and algorithmic advances – and appreciating the role of, and need for, advanced high-performance computing in addressing the given challenges.

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Source: PRACE