The Neil Ashton Podcast

This podcast focuses on explaining the fascinating ways that science and engineering change the world around us. In each episode, we talk to leading engineers from elite-level sports like cycling and Formula 1 to some of world's top academics to understand how fluid dynamics, machine learning & supercomputing are bringing in a new era of discovery. We also hear life stories, career advice and lessons they've learnt along the way that will help you to pursue a career in science and engineering.

The Neil Ashton Podcast is a science podcast hosted by Neil Ashton, with 34 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Neil Ashton hosts The Neil Ashton Podcast, a science show with 34 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #6

  S4 EP6 - Daniel Mira on Hydrogen Combustion Modelling and Future Propulsion

  Aug 11, 20261h 25mS4

  Hydrogen combustion, high-fidelity CFD and the future of aircraft propulsion are the focus of this conversation with Dr. Daniel Mira, Head of the Propulsion Technologies Group at the Barcelona Supercomputing Center. Neil and Dani discuss why reacting flows are so difficult to simulate, how hydrogen changes combustion and aircraft design, the limits of RANS, LES and DNS, GPU-native solvers, coding agents and AI surrogate models. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s4-e6-daniel-mira-on-hydrogen-combustion-modelling-and-future-propulsion/ Topics Why reacting flows are so computationally difficult Hydrogen versus hydrocarbon combustion When hydrogen could reach commercial aviation How engines and aircraft must be redesigned Industrial trust in high-fidelity combustion CFD RANS, LES and DNS for reacting flows Chemistry, load balancing and computational cost Wall modelling in combustion LES GPU acceleration and solver redesign Coding agents for scientific software AI surrogate models and digital engineering workflows Selected resources Daniel Mira and the Propulsion Technologies Group https://ptg.bsc.es/?p=44 Propulsion Technologies Group — research lines https://ptg.bsc.es/research-lines/ BSC — Combustion research https://www.bsc.es/research-development/research-areas/engineering-simulations/combustion Center of Excellence in Combustion (CoEC) https://coec-project.eu/ High-fidelity simulations of the mixing and combustion of a technically premixed hydrogen flame https://upcommons.upc.edu/entities/publication/08a27c10-cb13-4357-a3ab-8e9ec1d706cc Chapters 00:00 Podcast intro 00:39 Introducing Daniel Mira 03:00 Conversation begins 04:55 Why combustion CFD is so hard 10:23 Daniel’s path into hydrogen and jet-engine combustion 12:48 Hydrogen versus hydrocarbon combustion 17:58 Industrial adoption of hydrogen 20:54 Gas turbines, aviation and fuel infrastructure 25:35 How jet engines must change 30:43 Redesigning the whole aircraft 34:46 What will trigger commercial adoption? 37:27 Why aerospace projects take a decade 42:14 RANS, LES and DNS for reacting flows 44:31 Replacing expensive tests with high-fidelity CFD 46:01 The biggest accuracy gaps in combustion LES 49:26 Where the computational cost goes 52:06 Chemistry, species and source-term bottlenecks 55:35 Wall modelling in combustion LES 59:49 GPUs, algorithms and solver redesign 01:08:52 Can coding agents accelerate combustion CFD? 01:12:27 AI surrogate models for combustion 01:24:20 Closing thoughts

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  Episode #5

  S4 EP5 - Prof. Nils Thuerey on Differentiable Physics and Foundation Models

  Jul 23, 20261h 15mS4

  Differentiable physics, neural emulators and foundation models for PDEs are the focus of this conversation with Professor Nils Thuerey, head of the Physics-based Simulation group at TUM. Neil and Nils discuss PhiFlow, PICT, Tadpole, scalable 3D transformers, online synthetic data, open datasets, world models and agents that call physics simulators. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s4-e5-prof-nils-thuerey-on-differentiable-physics-and-foundation-models/ Topics Differentiable physics and physics-based deep learning PhiFlow and differentiable simulation across ML frameworks When neural emulators can outperform their training data Foundation models for PDEs and synthetic online training Scalable 3D transformers and high-resolution simulations LES, temporal data and correlated CFD datasets Open-source tools, startups and physics-aware world models AI agents that call physics simulators Papers Neural Emulator Superiority: When Machine Learning for PDEs Surpasses its Training Data https://arxiv.org/abs/2510.23111 Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning https://arxiv.org/abs/2605.15284 P3D: Scalable Neural Surrogates for High-Resolution 3D Physics Simulations with Global Context https://arxiv.org/abs/2509.10186 PICT — A Differentiable, GPU-Accelerated Multi-Block PISO Solver for Simulation-Coupled Learning Tasks in Fluid Dynamics https://arxiv.org/abs/2505.16992 PhiFlow: Differentiable Simulations for PyTorch, TensorFlow and JAX https://proceedings.mlr.press/v235/holl24a.html Physics-based Deep Learning https://arxiv.org/abs/2109.05237 Learning to Control PDEs with Differentiable Physics https://arxiv.org/abs/2001.07457 Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers https://arxiv.org/abs/2007.00016 tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow https://arxiv.org/abs/1801.09710 Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows https://arxiv.org/abs/1810.08217 WeatherBench: A Benchmark Dataset for Data-Driven Weather Forecasting https://arxiv.org/abs/2002.00469 SuperWing: A Comprehensive Transonic Wing Dataset for Data-Driven Aerodynamic Design https://arxiv.org/abs/2512.14397 Links Nils Thuerey and the Physics-based Simulation group https://ge.in.tum.de/about/n-thuerey/ Chapters 00:00 Podcast intro 00:39 Introducing Prof. Nils Thuerey 04:13 Conversation begins 05:13 From Computational Numerics to Graphics and Visual Effects 07:17 Physics-Based Deep Learning Before ChatGPT 10:01 CNNs, Graphics and the Move into Engineering Applications 12:37 PhiFlow and Differentiable Physics 14:13 Can Neural Emulators Surpass Their Training Data? 18:00 The Promise and Limits of Foundation Models for PDEs 20:43 Tadpole and Synthetic Online Pre-Training 24:07 From Canonical PDEs to Navier-Stokes and Industrial CFD 26:35 What Do Foundation Models Actually Learn? 28:36 PDE Pre-Training vs. Millions of CFD Simulations 33:08 Scaling 3D Transformers and Training Infrastructure 35:58 Generating and Training on Data in Real Time 38:00 LES, Temporal Data and Turbulence 42:15 Overfitting and Correlated Simulation Data 44:27 Bringing Differentiable Solvers Back into the Loop 45:31 WeatherBench, APEBench and the Value of Benchmarks 47:09 SuperWing, Open Datasets and Commercial Data 51:31 Open Source, Commercial Models and a Technical Oscar 56:17 Academia, Startups and Industry 01:00:55 What Will Change Over the Next Five Years? 01:02:07 World Models and the Need for Physics 01:08:19 Agents, Tool Use and Calling Physics Simulators 01:11:22 Career Advice for AI and Simulation 01:13:54 Closing Thoughts

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  Episode #4

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

  Jul 9, 20261h 26mS4

  In this episode, Professor Paola Cinnella - Professor of Fluid Mechanics at Sorbonne University and Director of the Sorbonne Cluster for Artificial Intelligence (SCAI) - joins Neil to discuss her path from classical fluid mechanics and high-order numerical methods into uncertainty quantification, Bayesian methods, data-driven turbulence modeling and AI for Science.Paola has built a career at the intersection of CFD, compressible and turbulent flows, dense gas dynamics, uncertainty quantification, robust optimization and machine learning. We discuss academic careers, dense gases, RANS uncertainty, AirfRANS, surrogate modeling, scientific publishing, education in the age of AI, and the idea of the "centaur scientist".Key topicsFluid mechanics, CFD and high-order schemesDense gases, real-gas effects and expansion shockwavesUncertainty quantification and Bayesian methodsRANS turbulence-model uncertaintyAirfRANS and CFD datasets for machine learningTurbulence modeling vs surrogate modelingScientific publishing and ML-for-CFD standardsSCAI and AI for ScienceEducation, ChatGPT and centaur scientistsPapersQuantification of model uncertainty in RANS simulations: A review - Heng Xiao, Paola Cinnellahttps://doi.org/10.1016/j.paerosci.2018.10.001Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression - Martin Schmelzer, Richard P. Dwight, Paola Cinnellahttps://doi.org/10.1007/s10494-019-00089-xBayesian estimates of parameter variability in the k-epsilon turbulence model - W.N. Edeling, P. Cinnella, R.P. Dwight, H. Bijlhttps://doi.org/10.1016/j.jcp.2013.10.027AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutionshttps://arxiv.org/abs/2212.07564Data-driven turbulence modeling - Paola Cinnellahttps://arxiv.org/abs/2404.09074Direct numerical simulations of supersonic turbulent channel flows of dense gases - Luca Sciacovelli, Paola Cinnella, Xavier Gloerfelthttps://doi.org/10.1017/jfm.2017.237LinksPaola Cinnella named Director of SCAIhttps://scai.sorbonne-universite.fr/news/paola-cinnella-new-directorSCAIhttps://scai.sorbonne-universite.fr/Paola Cinnella - HAL publicationshttps://cv.hal.science/paola-cinnellaPaola Cinnella - Google Scholarhttps://scholar.google.com/citations?hl=fr&user=wBRA0JAAAAAJERCOFTAC SIG 54 - Machine Learning for Fluid Dynamicshttps://www.ercoftac.org/special_interest_groups/54-machine-learning-for-fluid-dynamics/master-of-science-internships/Chapters00:00 Podcast intro00:39 Introducing Prof. Paola Cinnella03:28 Conversation begins03:56 How Paola found fluid mechanics07:09 Moving from Italy to France08:37 High-order schemes and compressible flows09:30 Building an academic career12:06 Dense gases and uncertainty quantification15:16 Expansion shockwaves and real-gas effects19:17 Returning to Paris and academic mobility24:52 Academia, passion and persistence27:51 Bayesian methods and turbulence uncertainty30:47 Learning statistics across disciplines33:07 LearnFluidS, AirfRANS and CFD datasets36:33 Skepticism and physics in ML turbulence modeling40:41 Could ML lead to a universal turbulence model?42:59 Turbulence models, surrogate models and RANS45:03 Why LES alone cannot solve optimization47:15 Multi-fidelity modeling49:08 What Computers & Fluids looks for in ML-for-CFD papers54:05 CFD metrics vs machine-learning metrics57:13 Overselling, publication pressure and quality62:22 SCAI and AI for Science66:07 Cross-disciplinary AI for Science69:26 Education in the AI era72:44 Critical thinking and AI outputs78:15 AI as a companion, not a replacement81:42 AlphaFold and the future of discovery83:43 Training centaur scientists85:11 Closing thoughts

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  Episode #3

  S4 EP3 - Prof. Ricardo Vinuesa on AI for Fluid Mechanics

  Jun 25, 20261h 6mS4

  In this episode, Professor Ricardo Vinuesa - Associate Chair for Research and Associate Professor of Aerospace Engineering at the University of Michigan - explores with Neil one of the biggest questions in modern fluid mechanics: can AI help us move beyond faster CFD and toward genuine autonomous scientific discovery? Drawing on his work at the intersection of turbulence, machine learning, explainable AI, reduced-order modeling, and flow control, Neil and Prof. Vineusa discusses the promise and limits of foundation models for fluids, why the right latent representations may matter more than simply scaling data, and how agentic AI systems could uncover physical mechanisms that humans might otherwise miss. Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations — Abhijeet Vishwasrao et al. https://arxiv.org/abs/2604.09584 The episode’s most direct follow-up: multi-agent LLMs and latent foundation models autonomously explore flow physics in a tandem-cylinder setup. Enhancing computational fluid dynamics with machine learning — Ricardo Vinuesa, Steven L. Brunton https://doi.org/10.1038/s43588-022-00264-7 A concise roadmap for useful ML in CFD, from faster simulations and turbulence modelling to reduced-order models. Identifying regions of importance in wall-bounded turbulence through explainable deep learning — Andrés Cremades et al. https://doi.org/10.1038/s41467-024-47954-6 Uses explainable AI to identify flow structures that matter for prediction and control, not just visually striking turbulence features. β-Variational autoencoders and transformers for reduced-order modelling of fluid flows — Alberto Solera-Rico et al. https://doi.org/10.1038/s41467-024-45578-4 Shows how disentangled latent spaces, autoencoders, and transformers can support interpretable reduced-order models of nonlinear flows. Improving turbulence control through explainable deep learning — Miguel Beneitez et al. https://arxiv.org/abs/2504.02354 Links explainable AI with deep reinforcement learning to target turbulence-sustaining mechanisms, with relevance for flow control, drag reduction, and energy efficiency.LinksVinuesaLabhttps://www.vinuesalab.com/Ricardo Vinuesa — University of Michigan Aerospace Engineeringhttps://aero.engin.umich.edu/people/ricardo-vinuesa/AI and ML for Fluid Dynamics course — Ricardo Vinuesa & Sergio Hoyashttps://www.flowthermolab.com/courses/ai-ml-for-fluids/VinuesaLab YouTube channelhttps://www.youtube.com/@VinuesaLabAI for Fluid Mechanics, Sustainability & XAI — Ricardo Vinuesahttps://www.youtube.com/watch?v=TOfwf4ffPnURicardo Vinuesa — Modelling and controlling turbulent flows through deep learninghttps://www.youtube.com/watch?v=0AOY_agZ8WMChapters00:00 Podcast Intro03:20 The Evolution of Foundation Models in Fluid Dynamics10:22 Understanding Explainable AI in Fluid Mechanics15:34 Challenges in Data Fidelity for Foundation Models20:29 Machine Learning vs. Reduced Order Modeling24:22 The Shift in Focus: Turbulence Modeling to Surrogate Models29:48 Exploring Agentic Systems for Scientific Discovery37:21 Exploring Latent Representations in Fluid Dynamics40:40 The Role of AI in Autonomous Discovery41:57 Bridging Fluid Mechanics and Computer Science45:28 Data-Driven vs Physics-Driven Models51:34 The Role of Academia in AI and Fluid Mechanics56:27 Optimization and Control in Machine Learning01:00:28 Future of AI in Fluid Dynamics: Beyond ChatGPT

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  Episode #2

  S4 EP2 - Prof. Nathan Kutz on Physics-Informed AI and Data-Driven Modeling

  Jun 11, 20261h 17mS4

  In this in-depth conversation, Professor J. Nathan Kutz — Director of Physics-Informed AI at Autodesk and one of the leading figures in data-driven modeling, dynamical systems, and scientific machine learning — shares his journey from academia to industry and reflects on how AI is reshaping engineering. Known for influential contributions to methods such as Dynamic Mode Decomposition and Sparse Identification of Nonlinear Dynamics, Kutz offers a rare perspective on the evolution of machine learning in the physical sciences, the role of physics in building trustworthy AI systems, and the future of automation, agents, and human expertise in engineering design.Key topicsHistory of machine learning in engineeringDynamic Mode Decomposition (DMD) and Sparse Identification of Nonlinear Dynamics (SINDy)Physics-informed AI and reduced order modelingThe debate between physics-based and data-driven modelsThe future of autonomous agents and their impact on industryPapers Flower discrimination by pollinators in a dynamic chemical environment — Jeffrey A. Riffell, Eli Shlizerman, Elischa Sanders, Leif Abrell, Billie Medina, Armin J. Hinterwirth, J. Nathan Kutz https://doi.org/10.1126/science.1251041 Nathan’s early move into neuroscience and data-driven biological modeling. Data assimilation and discrepancy modeling with shallow recurrent decoders — Yuxuan Bao, J. Nathan Kutz https://arxiv.org/abs/2512.01170 Using ML to close the gap between simulation and reality. Discovering governing equations from data by sparse identification of nonlinear dynamical systems — Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz https://doi.org/10.1073/pnas.1517384113 The foundational paper introducing SINDy. On Dynamic Mode Decomposition: Theory and Applications — Jonathan H. Tu, Clarence W. Rowley, Dirk M. Luchtenburg, Steven L. Brunton, J. Nathan Kutz https://doi.org/10.3934/jcd.2014.1.391 A key reference for Dynamic Mode Decomposition. Data-driven discovery of partial differential equations — Samuel H. Rudy, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz https://doi.org/10.1126/sciadv.1602614 Extends equation discovery to PDEs and physical systems. Deep learning for universal linear embeddings of nonlinear dynamics — Bethany Lusch, J. Nathan Kutz, Steven L. Brunton https://doi.org/10.1038/s41467-018-07210-0 Connects deep learning with Koopman theory. Articraft: An Agentic System for Scalable Articulated 3D Asset Generation — Matt Zhou, Ruining Li, Xiaoyang Lyu, Zhaomou Song, Zhening Huang, Chuanxia Zheng, Christian Rupprecht, Andrea Vedaldi, Shangzhe Wu https://arxiv.org/abs/2605.15187 Project page: https://articraft3d.github.io/ A practical example of agentic AI for engineering design.Chapters00:40 Introduction to Episode 05:00 Welcoming Prof Kutz10:34 The Evolution of Data-Driven Modeling16:13 Understanding the SINDy Algorithm and Its Implications22:14 Comparing Reduced Order Modeling and Modern Machine Learning28:29 The Role of Data in Machine Learning and Physics34:23 Challenges in Extrapolation and Real-World Applications40:46 Insights from McLaren and Team Dynamics46:07 The Shift from Academia to Industry48:53 Collaboration and Innovation in Engineering51:57 The Role of Human Expertise in Design54:45 Leveraging AI in Formula One57:32 The Future of AI and Workforce Dynamics59:06 Navigating Career Choices in a Changing Landscape01:03:02 The Evolution of Thought in Engineering01:09:06 Preparing for the Future of Technology01:14:04 Responsible Use of AI in Engineering

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