The Neil Ashton Podcast

Why leave NVIDIA to join Mistral?

In this episode, Neil Ashton explains his move to Mistral as VP of Computational Engineering, where he will work alongside Johannes Brandstetter on AI for engineering, physics foundation models and large language models.

Neil reflects on the career decisions that took him from Manchester and Formula 1 to Oxford, NASA collaborations, AWS and NVIDIA. He shares what he learned from moving between academia and industry, how open datasets became central to his work, and why the chance to keep learning and build something new made Mistral an exciting next step.

For students, researchers and engineers considering a change, the episode also offers a personal perspective on developing a specialism, keeping an academic mindset in industry, and staying open to opportunities without needing to plan your entire career in advance.

Topics include:

• Getting a first break in Formula 1
• Switching between academia and industry
• Learning HPC, cloud computing and product engineering
• Machine learning, open datasets and scientific collaboration
• NVIDIA's hardware, software and partnership ecosystem
• Why Mistral, and working with Johannes Brandstetter
• Career advice: expertise, continual learning and embracing change

Podcast archive: https://neilashton.co.uk/podcasts/

Chapters:
00:00 Podcast intro
00:39 From NVIDIA to Mistral
01:46 My new role: physics AI and LLMs
03:18 Starting out in engineering and motorsport
06:26 A PhD and finding a specialism
08:38 Getting a first break in Formula 1
09:52 Returning to academia
11:57 Oxford, HPC and NASA
15:21 Consulting, Formula 1 and the connection to AWS
19:39 Leaving Oxford and joining AWS
21:06 Product engineering, ML and open datasets
23:29 What I learned at NVIDIA
26:39 Why Mistral felt like the next step
29:51 Career advice: stay open to change
31:28 Becoming known for a specialism
32:41 Podcast plans and closing thoughts

Paper mentioned:
Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics
Neil Ashton, Johannes Brandstetter and Siddhartha Mishra
https://arxiv.org/abs/2511.20455

These are personal reflections on my own career and experience.

Should you still study engineering when AI can already write code, analyse data and automate parts of an engineer's job?

In this solo episode, Neil Ashton gives his view on engineering education and careers in the age of AI. His answer is yes—but the skill set is changing. Neil explains why engineering fundamentals still matter, where AI can act as an enabler, what students and early-career engineers should learn now, and why soft skills, projects and internships may become even more important.

Topics include:

- Why demand for engineers is likely to remain strong

- The engineering tasks most likely to change

- AI as an enabler for coding, CAD, CAE and automation

- Why domain knowledge is still essential for checking AI's work

- What practical AI fluency means beyond using a chat interface

- Advice for undergraduate, postgraduate and PhD students

- How projects, internships and soft skills can help you stand out

Podcast archive: https://neilashton.co.uk/podcasts/

Chapters:

00:00 Podcast intro

00:39 The career question in the age of AI

03:20 Why engineering demand is still growing

04:43 Which engineering tasks AI will change

05:21 AI as an engineering enabler

09:18 Why fundamentals and domain expertise still matter

11:59 AI fluency and the hiring market

16:41 Advice for students and researchers

20:05 What engineers should study now

22:11 Standing out: soft skills, projects and internships

25:41 Is engineering still worth it?

Resource mentioned:

- World Economic Forum, Future of Jobs Report 2025 — Skills outlook: https://www.weforum.org/publications/the-future-of-jobs-report-2025/in-full/3-skills-outlook/

Please note that this episode expresses my personal opinion and does not represent the views of NVIDIA.

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

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

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

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

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

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/

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/

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/

In this episode of the Neil Ashton podcast, the host revisits key trends in Computational Fluid Dynamics (CFD) from the past year, focusing on the rise of GPUs, advancements in AI and machine learning, the shift to cloud computing, the increasing adoption of high fidelity methods, and ongoing mergers and acquisitions in the industry. Each trend is explored in depth, highlighting the implications for the future of engineering and technology

Full episode, corrected transcript and resources:

https://neilashton.co.uk/podcasts/s3-e7-five-key-trends-for-cfd-revisited/

Professor Brian Launder, Professor at the University of Manchester and Fellow of the Royal Society and the Royal Academy of Engineering, reflects on more than 50 years of turbulence-modeling and CFD research. Neil and Brian discuss the influence of Professor Brian Spalding, the development of the k-epsilon and second-moment closure models, key collaborators and former students, and advice for early-career researchers.

Chapters

00:00 Podcast intro

00:30 Introduction

05:00 Early Academic Journey

10:06 Transition to MIT and Research Focus

16:21 Return to Imperial College and Early Career

21:06 Research Projects and PhD Students

27:46 Development of the k-epsilon model

33:18 CHAM and Career Changes

36:24 Move to UC Davis and New Research Directions

44:05 Challenges and Opportunities in Research

47:07 The Interview Experience

51:14 Transition to Manchester University

52:23 Research Innovations in Turbulence Modeling

57:45 The Development of the TCL Model

01:03:15 Nonlinear Eddy Viscosity Models

01:05:58 Advanced Wall Functions and Their Applications

01:10:09 Reflections on Career and Contributions

01:15:49 Legacy and Impact on Turbulence Modeling

Top Turbulence Modelling contributions (https://scholar.google.com/citations?user=Y3JbAK8AAAAJ&hl=en)

Full episode, corrected transcript and resources:

https://neilashton.co.uk/podcasts/s3-e6-prof-brian-launder-cfd-and-turbulence-modelling-pioneer/

In this episode, Joris Poort, CEO and founder of Rescale, shares his personal journey on founding Rescale as well as his thoughts on the future of CAE. He discusses the challenges of introducing HPC to the cloud market, the traits that make successful founders, and the importance of perseverance and execution in entrepreneurship. Joris reflects on the early days of Rescale, the significance of early investors, and the evolving landscape of cloud computing and AI integration in engineering. The conversation highlights the complexities of transitioning to cloud solutions and the future potential of HPC in various industries. In this conversation, Joris discusses the transformative impact of AI on engineering, particularly in the context of inference, simulation, and automation. He emphasizes the importance of efficiency in engineering processes and how AI can significantly reduce the time required for complex simulations. The discussion also touches on the cultural shifts within organizations as they adapt to AI technologies, the potential for AI surrogates to revolutionize engineering practices, and the challenges of closing the sim-to-real gap. Joris offers insights for aspiring founders, encouraging them to pursue meaningful work that can drive innovation and societal progress.

Chapters

00:00 Introductions
03:30 The Genesis of Rescale: A Cloud Computing Journey
05:21 From Engineering to Entrepreneurship: The Leap of Faith
09:28 Traits of a Successful Founder: Courage and Perseverance
14:51 Tactical Steps to Startup Success: Building from the Ground Up
22:10 Milestones and Breakthroughs: The Early Days of Rescale
30:54 Navigating Challenges: The Role of Cloud Providers in HPC
35:24 The Intersection of HPC and AI Training
37:05 Cloud vs On-Premise: The Cost Debate
39:54 Complexities of HPC in Enterprises
42:27 The Slow Shift to Cloud Adoption
44:34 Optimizing Workloads with Rescale
46:50 Usability Challenges in Enterprise Software
48:32 The Rise of Neo Clouds and Competition
51:18 Speed and Efficiency in AI Training
54:34 AI's Transformative Impact on Engineering
58:54 The Future of AI Surrogates in Design
01:03:28 Agentic AI: The New Paradigm in Engineering
01:14:21 Solving Real Business Problems
01:19:26 The Impact of AI on Engineering
01:22:27 Innovation in Aerospace and Beyond
01:25:19 Cultural Change in Organizations
01:28:34 The Future of AI and Engineering
01:39:09 Advice for Aspiring Founders

Keywords

HPC, cloud computing, startup journey, Rescale, entrepreneurship, AI, technology, innovation, engineering, business, AI, engineering, inference, simulation, automation, digital twin, innovation, aerospace, machine learning, technology

Full episode, corrected transcript and resources:

https://neilashton.co.uk/podcasts/s3-e5-joris-poort-ceo-and-founder-of-rescale/

In this episode of the Neil Ashton podcast, Neil discusses the impact of AI on CAE engineering, providing five essential tips for engineers to thrive in this evolving landscape. The conversation covers the importance of maintaining an open mind, continuous education, and preparing for AI physics applications. It also delves into the build vs. buy dilemma for AI solutions and the emerging concept of agentic AI, which promises to revolutionize engineering practices.

Chapters

00:00 Introduction to the Podcast and AI in Engineering

01:03 Five Tips for CAE Engineers in the Era of AI

01:24 1: Keeping an Open Mind

07:39 2: Understanding AI Physics and Its Applications

13:30 3: Preparing for AI Implementation in Engineering

18:54 4: The Build vs. Buy Dilemma in AI Solutions

22:20 5: The Future of Agentic AI in Engineering

Full episode, corrected transcript and resources:

https://neilashton.co.uk/podcasts/s3-e4-five-tips-for-cae-engineers-in-the-era-of-ai/

In this conversation, Neil Ashton interviews Prof. Johannes Brandstetter, a physicist turned machine learning expert, about his journey from academia to industry, focusing on the application of machine learning in engineering and computational fluid dynamics (CFD). They discuss the Aurora project, the challenges of integrating machine learning with engineering, and the importance of data in training models. Johannes shares insights on the use of transformers in modeling, the significance of resolution independence, and the role of open-source practices in advancing the field. The conversation also touches on the challenges of founding a startup and the need for multidisciplinary collaboration in tackling complex engineering problems.

Links:

Github: https://brandstetter-johannes.github.io
Emmi AI: https://www.emmi.ai
Google scholar: https://scholar.google.com/citations?user=KiRvOHcAAAAJ&hl=de

AB-UPT transform paper: https://arxiv.org/abs/2502.09692

Chapters

00:00 Introduction to Johannes Brandstetter
07:10 The Aurora Project and Key Learnings
11:15 Machine Learning in Engineering and CFD
17:19 Challenges with Mesh Graph Networks
20:16 Transformers in Physics Modeling
31:14 Tokenization in CFD with Transformers
39:58 Challenges in High-Dimensional Meshes
41:08 Inference Time and Mesh Generation
41:36 Neural Operators and CAD Geometry
45:59 Anchor Tokens and Scaling in CFD
48:40 Data Dependency and Multi-Fidelity Models
50:32 The Role of Physics in Machine Learning
54:28 Temporal Modeling in Engineering Simulations
56:58 Learning from Temporal Dynamics
1:00:58 Stability in Rollout Predictions
1:03:48 Multidisciplinary Approaches in Engineering
1:05:18 The Startup Journey and Lessons Learned

Full episode, corrected transcript and resources:

https://neilashton.co.uk/podcasts/s3-e3-prof-johannes-brandstetter-on-ai-for-computational-fluid-dynamics/

In this episode of the Neil Ashton podcast, Professor Russell Cummings shares his extensive journey through the fields of aerodynamics, computational fluid dynamics and hypersonics. He discusses his early inspirations, his early days at University and the Hughes Aircraft Company - a key time during this life. He also talks about the cyclical nature of hypersonics research, and the challenges faced in computational fluid dynamics (CFD). Prof. Cummings emphasizes the importance of perseverance in engineering careers and the need for collaboration between experimental and computational methods. He also shares insights on the role of AI in hypersonics and offers valuable advice for aspiring engineers.

Prof. Russ Cummings graduated from California Polytechnic State University (Cal Poly) with a B.S. and M.S. in Aeronautical Engineering, before receiving his Ph.D. in Aerospace Engineering from the University of Southern California; he also received a B.A. in music from Cal Poly. He is currently Professor of Aeronautics at the U.S. Air Force Academy and Director of the Hypersonic Vehicle Simulation Institute. Prior to this he was Professor of Aerospace Engineering at Cal Poly, where he also served as department chairman for four years. He also worked at Hughes Aircraft Company, and completed a National Research Council postdoctoral research fellowship at NASA Ames Research Center, working on the computation of high angle-of-attack flowfields. He is a Fellow of the Royal Aeronautical Society and the American Institute of Aeronautics and Astronautics.

Distribution Statement A: approved for public release, PA# USAFA-DF-2025-652. The views expressed in this interview are those of the author and do not necessarily reflect the official policy or position of the United States Air Force Academy, the Air Force, the Department of Defense, or the U.S. Government.

Links

Aerodynamics for engineers: https://www.cambridge.org/us/universitypress/subjects/engineering/aerospace-engineering/aerodynamics-engineers-7th-edition?format=HB&isbn=9781009501309
RAeS Lanchester Named Lecture 2024: Frederick W. Lanchester and 'Aerodynamics' https://www.youtube.com/watch?app=desktop&v=lApNzYaZOmk&t=884s
NASA at 50 (Prof Cummings is in the picture): https://images.nasa.gov/details/ARC-1989-AC89-0276-6

Chapters

00:00 Introduction to the Podcast and Guest
04:56 Professor Russell Cummings: A Journey Through Engineering
31:14 The Evolution of Hypersonics Research
58:26 The Role of AI in Hypersonics and CFD
01:37:55 Advice for Aspiring Engineers

Full episode, corrected transcript and resources:

https://neilashton.co.uk/podcasts/s3-e2-prof-russell-cummings-aerospace-engineering-and-hypersonics/

In this episode of the Neil Ashton podcast, Professor Mike Giles shares his extensive journey through the fields of computational fluid dynamics (CFD), computational finance and HPC. He discusses his early academic influences, his early days at Cambridge, internships at Rolls-Royce, his transition to MIT and Oxford where he made significant contributions to high-performance computing and numerical analysis. The conversation highlights his hands-on approach to research and teaching, as well as his pioneering work in Monte Carlo methods and GPU computing. This conversation explores the journey of a mathematician and engineer from MIT to Rolls-Royce and then to Oxford, highlighting the evolution of computational engineering, the development of the Hydra code, and the transition from CFD to financial applications. In this conversation, the speaker reflects on their journey through burnout, career transitions, and the evolution of their work in computational finance and numerical analysis. They discuss the challenges of managing large software projects, the shift from Hydra code development to finance, and the integration of advanced methodologies in their work. The conversation also touches on the role of high-performance computing, the impact of AI on research, and advice for the next generation of students pursuing careers in mathematics and programming.

Links:
https://people.maths.ox.ac.uk/gilesm/

Chapters

00:00 Introduction
06:25 Professor Mike Giles: A Journey Through CFD and Finance
17:30 Early Academic Influences and Career Path
29:34 Transition to MIT and Early Research
40:01 High-Performance Computing and Its Impact
41:30 Navigating Between MIT and Rolls-Royce
44:54 The Evolution of Research at MIT
48:47 Transitioning to Oxford and the Role of Rolls-Royce
51:07 The Genesis of the Hydra Code
01:00:47 The Role of Conferences in Engineering
01:10:58 The Shift from CFD to Financial Applications
01:21:30 Navigating Burnout and Career Transitions
01:24:04 Shifting Focus: From Hydrocode to Computational Finance
01:29:30 Bridging Mathematics and Finance: Methodologies and Techniques
01:35:09 The Role of High-Performance Computing in Modern Research
01:39:20 AI's Impact on Research and Future Directions
01:54:00 Advice for the Next Generation: Pursuing Passion and Skills

Full episode, corrected transcript and resources:

https://neilashton.co.uk/podcasts/s3-e1-prof-mike-giles-a-cfd-and-computational-finance-pioneer/

In this episode of the Neil Ashton podcast, the discussion revolves around foundational models in fluid dynamics, particularly in the context of computational fluid dynamics (CFD). Neil shares insights from a recent panel discussion and explores the potential of AI in predicting fluid behavior. He discusses the evolution of AI in CFD, the challenges of data availability, and the differing adoption rates between industries. The episode concludes with predictions about the future of foundational models and their impact on the engineering landscape.

Chapters

00:00 Introduction to the Podcast and Topic
01:09 Foundational Models in Fluid Dynamics
10:09 The Evolution of AI in CFD
19:52 Future Predictions and Industry Dynamics

Full episode, corrected transcript and resources:

https://neilashton.co.uk/podcasts/s2-e11-foundational-ai-models-for-fluids/

In this episode of the Neil Ashton podcast, Neil discusses the intersection of cycling and engineering with Kurt Bergin-Taylor, head of innovation at Tudor Pro Cycling. They explore how technology and science are transforming cycling into a more competitive and innovative sport, akin to Formula One. The conversation covers various aspects of cycling, including the importance of aerodynamics, nutrition, and the holistic approach to rider performance. Kurt shares insights from his academic background and experiences in professional cycling, emphasizing the need for tailored training and the integration of technology in enhancing performance. They discuss the future of cycling innovation, emphasizing the importance of individualization in gear, collaborative relationships with partners, and the evolving mindset of young cyclists. Kurt highlights the significance of data and AI in optimizing performance and strategies in cycling, while also addressing the need for viewer engagement in the sport. Finally Kurt shares valuable advice for aspiring engineers looking to enter the cycling industry, stressing the importance of mentorship and practical experience.

Chapters

00:00 Introduction to the Podcast and Themes
04:55 Kurt Bergin-Taylor: Background and Role at Tudor Pro Cycling
10:08 The Structure and Dynamics of a Pro Cycling Team
12:59 Innovation in Cycling: Aerodynamics, Thermal Management, and Safety
19:14 Nutrition, Training, and Performance in Cycling
29:18 Future Innovations in Cycling Equipment and Systems
30:42 Understanding Individualization in Cycling Gear
34:30 Collaborative Innovation in Cycling Equipment
38:20 The Evolving Mindset of Young Cyclists
42:28 Enhancing Viewer Engagement in Cycling
46:24 The Future of Data and AI in Cycling
50:05 Advice for Aspiring Engineers in Cycling

Takeaways

- Cycling is increasingly influenced by technology and engineering.
- Tudor Pro Cycling is focused on long-term performance and innovation.
- Aerodynamics plays a crucial role in cycling performance.
- Thermal management is essential for riders in extreme conditions.
- Nutrition has dramatically improved in cycling over the last decade.
- Training methodologies must be tailored to individual riders.
- The relationship between power output and speed is complex.
- Safety innovations are critical as speeds increase in cycling.
- Understanding the whole system of rider and equipment is vital.
- Professional cyclists have different recovery capabilities compared to amateurs. Individualization in cycling gear is crucial for performance.
- Collaborative innovation with partners enhances product development.
- Young cyclists are more educated but sometimes overlook tactical aspects.
- Data-driven insights are essential for optimizing race strategies.
- Viewer engagement can be improved through real-time data sharing.
- AI and machine learning are emerging tools in cycling optimization.
- Mentorship is vital for aspiring professionals in the cycling industry.
- Practical experience and initiative can open doors in professional sports.
- Cycling offers a holistic approach to engineering and performance.
- The cycling industry is growing, providing more opportunities for engineers.

Full episode, corrected transcript and resources:

https://neilashton.co.uk/podcasts/s2-e10-dr-kurt-bergin-taylor-head-of-innovation-tudor-pro-cycling/

A short episode to give a brief update on what I've been doing and to say sorry for not putting out episodes recently. I've joined NVIIDA as a Distinguished CAE Architect and have been rather busy! New episodes will be coming soon! Listen to the episode to learn more.

Full episode, corrected transcript and resources:

https://neilashton.co.uk/podcasts/s2-e9-new-job-update/