Prof. Karthik Duraisamy is a Professor at the University of Michigan, the Director of the Michigan Institute for Computational Discovery and Engineering (MICDE) and the founder of the startup Geminus.AI. In this episode, we discusses AI4Science, with a particular focus on fluid dynamics and computational fluid dynamics. Prof. Duraisamy talks about the progress and challenges of using machine learning in turbulence modeling and the potential of surrogate models (both data-driven and physics-informed neural networks). He also explores the concept of foundational models for science and the role of data and physics in AI applications. The discussion highlights the importance of using machine learning as a tool in the scientific process and the potential benefits of large language models in scientific discovery. We also discuss the need for collaboration between academia, tech companies, and startups to achieve the vision of a new platform for scientific discovery. Prof. Duraisamy predicts that in the next few years, there may be major advancements in foundation models for science however he cautions against unrealistic expectations and emphasizes the importance of understanding the limitations of AI.

Links:
Summer school tutorials https://github.com/scifm/summer-school-2024 (scroll down for links to specific tutorials)
SciFM24 recordings : https://micde.umich.edu/news-events/annual-symposia/2024-symposium/
SciFM24 Summary : https://drive.google.com/file/d/1eC2HJdpfyZZ42RaT9KakcuACEo4nqAsJ/view
Trillion parameter consortium : https://tpc.dev
Turbulence Modelling in the age of data: https://www.annualreviews.org/content/journals/10.1146/annurev-fluid-010518-040547
LinkedIn: https://www.linkedin.com/showcase/micde/

Chapters

00:00 Introduction
09:41 Turbulence Modeling and Machine Learning
21:30 Surrogate Models and Physics-Informed Neural Networks
28:42 Foundational Models for Science
35:23 The Power of Large Language Models
47:43 Tools for Foundation Models
48:39 Interfacing with Specialized Agents
53:31 The Importance of Collaboration
58:57 The Role of Agents and Solvers
01:08:26 Balancing AI and Existing Expertise
01:21:28 Predicting the Future of AI in Fluid Dynamics
01:23:18 Closing Gaps in Turbulence Modeling
01:25:42 Achieving Productivity Benefits with Existing Tools

Takeaways

-Machine learning is a valuable tool in the development of turbulence modeling and other scientific applications.
-Data-driven modeling can provide additional insights and improve the accuracy of scientific models.
-Physics-informed neural networks have potential in solving inverse problems but may not be as effective in solving complex PDEs.
-Foundational models for science can benefit from a combination of data-driven approaches and physics-based knowledge.
-Large language models have the potential to assist in scientific discovery and provide valuable insights in various scientific domains. Having a strong foundation in the domain of study is crucial before applying AI techniques.
-Collaboration between academia, tech companies, and startups is necessary to achieve the vision of a new platform for scientific discovery.
-Understanding the limitations of AI and managing expectations is important.
-AI can be a valuable tool for productivity gains and scientific assistance, but it will not replace human expertise.

Keywords

#computationalfluiddynamics , #ailearning #largelanguagemodels , #cfd , #supercomputing , #fluiddynamics