In recent years we have seen rapid growth in interest in artificial intelligence in general, and machine learning (ML) techniques, particularly in different branches of science and engineering. The rapid growth of the Scientific Machine Learning field derives from the combined development and use of efficient data analysis algorithms, the availability of data from scientific instruments and computer simulations, and advances in high-performance computing. On May 25 2023, COPPE/UFRJ organized a forum to discuss Artificial Intelligence developments and its impact on the society [*].
As the coordinator of the High Performance Computing Center (Nacad) at COPPE/UFRJ, Alvaro Coutinho, presented advances in AI in Engineering and the importance of multidisciplinary research networks to address current issues in Scientific Machine Learning. Alvaro took the opportunity to highlight the need for Brazil to invest in high performance computing capacity.
The country’s sovereignty needs autonomy in producing ML advances, which depends on HPC support at the Universities and Research Centers. Brazil has nine machines in the Top 500 list of the most powerful computer systems in the world, but almost all at Petrobras company, and Universities need much more. ML is well-known to require HPC, when combined to scientific computer simulations it becomes essential.
The conventional notion of ML involves training an algorithm to automatically discover patterns, signals, or structures that may be hidden in huge databases and whose exact nature is unknown and therefore cannot be explicitly programmed. This field may face two major drawbacks: the need for a significant volume of (labelled) expensive to acquire data and limitations for extrapolating (making predictions beyond scenarios contained in the trained data difficult).
Considering that an algorithm’s predictive ability is a learning skill, current challenges must be addressed to improve the analytical and predictive capacity of Scientific ML algorithms, for example, to maximize its impact in applications of renewable energy. References [1-5] illustrate recent advances in Scientific Machine Learning in different areas of engineering and computer science.
 Baker, Nathan, Steven L. Brunton, J. Nathan Kutz, Krithika Manohar, Aleksandr Y. Aravkin, Kristi Morgansen, Jennifer Klemisch, Nicholas Goebel, James Buttrick, Jeffrey Poskin, Agnes Blom-Schieber, Thomas Hogan, Darren McDonaldAlexander, Frank, Bremer, Timo, Hagberg, Aric, Kevrekidis, Yannis, Najm, Habib, Parashar, Manish, Patra, Abani, Sethian, James, Wild, Stefan, Willcox, Karen, and Lee, Steven. Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. United States: N. p., 2019. Web. doi:10.2172/1478744.
 Brunton, Steven L., Bernd R. Noack, and Petros Koumoutsakos. “Machine learning for fluid mechanics.” Annual Review of Fluid Mechanics 52 (2020): 477-508.
 Karniadakis, George Em, et al. “Physics-informed machine learning.” Nature Reviews Physics 3.6 (2021): 422-440.
 Inria White Book on Artificial Intelligence: Current challenges and Inria’s engagement, 2nd edition, 2021. URL: https://www.inria.fr/en/white-paper-inria-artificial-intelligence
 Silva, Romulo, Umair bin Waheed, Alvaro Coutinho, and George Em Karniadakis. “Improving PINN-based Seismic Tomography by Respecting Physical Causality.” In AGU Fall Meeting Abstracts, vol. 2022, pp. S11C-09. 2022.