S4 EP4 - Prof. Paola Cinnella on AI for Science and Fluid Mechanics

RANS uncertainty, data-driven turbulence modeling and AI for Science are the focus of this conversation with Professor Paola Cinnella, Professor of Fluid Mechanics at Sorbonne University and Director of SCAI. Neil and Paola discuss high-order methods, dense gases, Bayesian uncertainty, AirfRANS, surrogate modeling, scientific publishing and education in the AI era. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s4-e4-prof-paola-cinnella-on-ai-for-science-and-fluid-mechanics/ Topics Fluid mechanics, CFD and high-order schemes Dense gases, real-gas effects and expansion shockwaves Uncertainty quantification and Bayesian methods RANS turbulence-model uncertainty AirfRANS and CFD datasets for machine learning Turbulence modeling vs. surrogate modeling Scientific publishing and ML-for-CFD standards SCAI and AI for Science Education, ChatGPT and centaur scientists Papers Quantification of model uncertainty in RANS simulations: A review — Heng Xiao, Paola Cinnella https://doi.org/10.1016/j.paerosci.2018.10.001 Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression — Martin Schmelzer, Richard P. Dwight, Paola Cinnella https://doi.org/10.1007/s10494-019-00089-x Bayesian estimates of parameter variability in the k-epsilon turbulence model — W.N. Edeling, P. Cinnella, R.P. Dwight, H. Bijl https://doi.org/10.1016/j.jcp.2013.10.027 AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions https://arxiv.org/abs/2212.07564 Data-driven turbulence modeling — Paola Cinnella https://arxiv.org/abs/2404.09074 Direct numerical simulations of supersonic turbulent channel flows of dense gases — Luca Sciacovelli, Paola Cinnella, Xavier Gloerfelt https://doi.org/10.1017/jfm.2017.237 Links Paola Cinnella named Director of SCAI https://scai.sorbonne-universite.fr/news/paola-cinnella-new-director SCAI https://scai.sorbonne-universite.fr/ Paola Cinnella — HAL publications https://cv.hal.science/paola-cinnella Paola Cinnella — Google Scholar https://scholar.google.com/citations?hl=fr&user=wBRA0JAAAAAJ ERCOFTAC SIG 54 — Machine Learning for Fluid Dynamics https://www.ercoftac.org/special_interest_groups/54-machine-learning-for-fluid-dynamics/master-of-science-internships/ Chapters 00:00 Podcast intro 00:39 Introducing Prof. Paola Cinnella 03:28 Conversation begins 03:56 How Paola Found Fluid Mechanics 07:09 Moving from Italy to France 08:37 High-Order Schemes and Compressible Flows 09:30 Building an Academic Career 12:06 Dense Gases and Uncertainty Quantification 15:16 Expansion Shockwaves and Real-Gas Effects 19:17 Returning to Paris and Academic Mobility 24:52 Academia, Passion and Persistence 27:51 Bayesian Methods and Turbulence Uncertainty 30:47 Learning Statistics Across Disciplines 33:07 LearnFluidS, AirfRANS and CFD Datasets 36:33 Skepticism and Physics in ML Turbulence Modeling 40:41 Could ML Lead to a Universal Turbulence Model? 42:59 Turbulence Models, Surrogate Models and RANS 45:03 Why LES Alone Cannot Solve Optimization 47:15 Multi-Fidelity Modeling 49:08 What Computers & Fluids Looks for in ML-for-CFD Papers 54:05 CFD Metrics vs. Machine-Learning Metrics 57:13 Overselling, Publication Pressure and Quality 01:02:22 SCAI and AI for Science 01:06:07 Cross-Disciplinary AI for Science 01:09:26 Education in the AI Era 01:12:44 Critical Thinking and AI Outputs 01:18:15 AI as a Companion, Not a Replacement 01:21:42 AlphaFold and the Future of Discovery 01:23:43 Training Centaur Scientists 01:25:11 Closing Thoughts