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/