S1, EP12 - Prof Karthik Duraisamy - Scientific Foundation Models

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 discuss AI for Science, 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 foundation 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 Foundation 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.

-Foundation 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

Full episode, corrected transcript and resources:

https://neilashton.co.uk/podcasts/s1-e12-prof-karthik-duraisamy-scientific-foundation-models/