The Neil Ashton Podcast

Neil Ashton
  • 5.0 (3)
  • Natural Sciences
  • Updated Biweekly

The Neil Ashton Podcast explores artificial intelligence, computational engineering, computational fluid dynamics, scientific machine learning, and high-performance computing. Hosted by Neil Ashton, a Distinguished Engineer at NVIDIA, it features conversations with leading researchers and engineers about technology, careers, and scientific discovery.

  1. 11h ago

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

    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

  2. Jul 23

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

    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

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

  3. Jul 9

    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

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

  4. Jun 25

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

    Foundation models, explainable AI and autonomous discovery in fluid mechanics are the focus of this conversation with Professor Ricardo Vinuesa, Associate Chair for Research and Associate Professor of Aerospace Engineering at the University of Michigan. Neil and Ricardo discuss latent representations, turbulence, reduced-order modeling, flow control and whether AI can discover physical mechanisms that humans might miss. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s4-e3-prof-ricardo-vinuesa-on-ai-for-fluid-mechanics/ Topics Can fluid mechanics have a “ChatGPT moment”? Foundation models and latent representations for turbulent flows Explainable AI, causality and identifying the mechanisms that matter Why classical coherent structures may tell only part of the turbulence story Physics-informed vs purely data-driven machine learning Reduced-order modeling, autoencoders, transformers and nonlinear compression Deep reinforcement learning for flow control and optimization Agentic AI and autonomous scientific discovery in PDE-governed systems How academia, computer science and engineering education must adapt to AI Papers Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations — Abhijeet Vishwasrao et al. https://arxiv.org/abs/2604.09584 Multi-agent LLMs and latent foundation models autonomously explore flow physics in a tandem-cylinder problem. Enhancing computational fluid dynamics with machine learning — Ricardo Vinuesa, Steven L. Brunton https://doi.org/10.1038/s43588-022-00264-7 A roadmap for useful ML in CFD, including faster simulations, turbulence models and 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 Explainable AI identifies flow structures that matter for prediction and control. 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 Disentangled latent spaces, autoencoders and transformers support interpretable reduced-order models. Improving turbulence control through explainable deep learning — Miguel Beneitez et al. https://arxiv.org/abs/2504.02354 Explainable AI and deep reinforcement learning target turbulence-sustaining mechanisms. Links VinuesaLab https://www.vinuesalab.com/ Ricardo Vinuesa — University of Michigan Aerospace Engineering https://aero.engin.umich.edu/people/ricardo-vinuesa/ AI and ML for Fluid Dynamics course — Ricardo Vinuesa and Sergio Hoyas https://www.flowthermolab.com/courses/ai-ml-for-fluids/ VinuesaLab YouTube channel https://www.youtube.com/@VinuesaLab AI for Fluid Mechanics, Sustainability & XAI — Ricardo Vinuesa https://www.youtube.com/watch?v=TOfwf4ffPnU Modelling and controlling turbulent flows through deep learning — Ricardo Vinuesa https://www.youtube.com/watch?v=0AOY_agZ8WM Chapters 00:00 Podcast intro 03:20 The Evolution of Foundation Models in Fluid Dynamics 10:22 Understanding Explainable AI in Fluid Mechanics 15:34 Challenges in Data Fidelity for Foundation Models 20:29 Machine Learning vs. Reduced-Order Modeling 24:22 The Shift from Turbulence Modeling to Surrogate Models 29:48 Exploring Agentic Systems for Scientific Discovery 37:21 Exploring Latent Representations in Fluid Dynamics 40:40 The Role of AI in Autonomous Discovery 41:57 Bridging Fluid Mechanics and Computer Science 45:28 Data-Driven vs. Physics-Driven Models 51:34 The Role of Academia in AI and Fluid Mechanics 56:27 Optimization and Control in Machine Learning 01:00:28 The Future of AI in Fluid Dynamics: Beyond ChatGPT

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

  5. Jun 11

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

    Physics-informed AI, DMD, SINDy and data-driven engineering are the focus of this conversation with Professor J. Nathan Kutz, Director of Physics-Informed AI at Autodesk. Neil and Nathan trace machine learning’s evolution in engineering, the role of physics in trustworthy models, and the future of autonomous agents, design automation and human expertise. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s4-e2-prof-nathan-kutz-on-physics-informed-ai-and-data-driven-modeling/ Topics History of machine learning in engineering Dynamic Mode Decomposition (DMD) and Sparse Identification of Nonlinear Dynamics (SINDy) Physics-informed AI and reduced-order modeling The debate between physics-based and data-driven models The future of autonomous agents and their impact on industry Papers 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 A practical example of agentic AI for engineering design. Links Articraft project page https://articraft3d.github.io/ Chapters 00:00 Podcast intro 00:40 Introduction to Episode 05:00 Welcoming Prof. Kutz 10:34 The Evolution of Data-Driven Modeling 16:13 Understanding the SINDy Algorithm and Its Implications 22:14 Comparing Reduced-Order Modeling and Modern Machine Learning 28:29 The Role of Data in Machine Learning and Physics 34:23 Challenges in Extrapolation and Real-World Applications 40:46 Insights from McLaren and Team Dynamics 46:07 The Shift from Academia to Industry 48:53 Collaboration and Innovation in Engineering 51:57 The Role of Human Expertise in Design 54:45 Leveraging AI in Formula One 57:32 The Future of AI and Workforce Dynamics 59:06 Navigating Career Choices in a Changing Landscape 01:03:02 The Evolution of Thought in Engineering 01:09:06 Preparing for the Future of Technology 01:14:04 Responsible Use of AI in Engineering

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

  6. Jun 1

    S4 EP1 - Are AI Agents and Foundation Models About to Rewrite CAE?

    In this episode, Neil explores how agents, foundation models, and AI are set to transform the Computer-Aided Engineering (CAE) and Electronic Design Automation (EDA) landscapes. He shares a comprehensive historical perspective and predicts a near-future where AI-driven automation redefines engineering workflows, productivity, and innovation. Main Topics: The evolution of simulation codes from the 1960s to modern commercial software The rise of cloud computing, GPUs, and their impact on CAE and EDA industries The integration of AI, surrogate modeling, and foundation models into simulation workflows The emergence of agentic AI systems capable of autonomously performing complex engineering tasks The strategic responses of major software companies to AI and agent technologies The potential democratization and automation of engineering design through AI agents Critical questions on model ownership, transparency, and industry adoption Timestamps: 00:00 - Podcast intro 00:40 - Introduction: How agents and foundation models will disrupt CAE & EDA 01:40 - Historical overview: From code writing in the 60s to commercial software 03:10 - Growth of aerospace and automotive industry codes and commercialization 04:40 - The impact of HPC, cloud computing, and hardware evolution 06:25 - Rise of cloud SaaS models and "sassification" of simulation tools 07:40 - Big tech entrance: AWS, Microsoft, and Google in CAE & EDA 09:00 - GPU acceleration: Changed landscape in past three to four years 09:10 - The role of AI startups offering surrogate models and real-time simulation 10:40 - Industry consolidation: Mergers and acquisitions among software giants 11:40 - The emergence of foundation models and surrogate systems in simulation 13:00 - The significance of agents: Combining AI, models, and automation 14:10 - Capabilities of autonomous AI agents in complex engineering workflows 15:25 - Practical use cases: Running simulations, setting up experiments, and data analysis 16:10 - Questions about model ownership, open-source codes, and licensing 16:40 - How agent-driven automation could democratize engineering expertise 19:40 - The future of AI in engineering: Collaboration, transparency, and scientific rigor 21:25 - Final thoughts: Opportunities, challenges, and the transformative potential of AI Please note that this episode expresses my personal opinion and does not represent the views of NVIDIA. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s4-e1-are-ai-agents-and-foundation-models-about-to-rewrite-cae/

    S4 EP1 - Are AI Agents and Foundation Models About to Rewrite CAE?

  7. 12/02/2025

    S3 EP9 - Fluid Intelligence with Johannes Brandstetter and Siddhartha Mishra

    In this conversation, Neil Ashton and Prof. Siddhartha Mishra, and Prof. Johannes Brandstetter discuss their recent paper on AI foundation models in computational fluid dynamics (CFD). They explore the backgrounds of the speakers, the journey to writing the paper, the role of AI in CFD, and the challenges of scaling laws and data generation. The discussion also covers model training costs, open questions, and future directions for research in this field. Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics : https://arxiv.org/abs/2511.20455v1 Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s3-e9-fluid-intelligence-with-johannes-brandstetter-and-siddhartha-mishra/

    S3 EP9 - Fluid Intelligence with Johannes Brandstetter and Siddhartha Mishra

  8. 11/01/2025

    S3 EP8 - The Conference Connection (HPC, CAE, ML & Engineering)

    In this episode, Neil Ashton discusses various conferences and workshops in the automotive, aerospace, and machine learning fields. He highlights the importance of these events for networking, education, and staying updated with industry trends. From the SAE and AIAA events to machine learning workshops, Neil provides insights into what attendees can expect and the value of participating in these gatherings. Full episode, corrected transcript and resources: https://neilashton.co.uk/podcasts/s3-e8-the-conference-connection-hpc-cae-ml-and-engineering/

    S3 EP8 - The Conference Connection (HPC, CAE, ML & Engineering)
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Ratings & Reviews

5
out of 5
3 Ratings

About

The Neil Ashton Podcast explores artificial intelligence, computational engineering, computational fluid dynamics, scientific machine learning, and high-performance computing. Hosted by Neil Ashton, a Distinguished Engineer at NVIDIA, it features conversations with leading researchers and engineers about technology, careers, and scientific discovery.

Information

  • Creator
    Neil Ashton
  • Years Active
    2024 - 2026
  • Episodes
    40
  • Rating
    Clean
  • Copyright
    © Neil Ashton
  • Show Website
    The Neil Ashton Podcast

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