Neil Ashton | AI, CFD & Computational Engineering

Why I Left NVIDIA for Mistral

2.5K views · 4w ago · 33:32

Why leave NVIDIA to join Mistral? In this episode, Neil Ashton explains the move, his new role as VP of Computational Engineering, and the opportunity to build AI for engineering alongside Johannes Brandstetter. Neil looks back at the decisions that took him from an engineering degree and PhD in Manchester to Formula 1, research at Oxford, NASA collaborations, cloud computing at AWS, and accelerated computing at NVIDIA.

Topics: Podcast intro · From NVIDIA to Mistral · My new role: physics AI and LLMs · Starting out in engineering and motorsport · A PhD and finding a specialism · Getting a first break in Formula 1

Should You Still Study Engineering in the Age of AI?

1.9K views · 2mo ago · 27:53

Full episode, transcript and resources 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.

Topics: The career question in the age of AI · Why engineering demand is still growing · Which engineering tasks AI will change · AI as an engineering enabler · Why fundamentals and domain expertise still matter · AI fluency and the hiring market

Can CFD Predict Hydrogen Combustion? GPUs, AI & Future Propulsion | Daniel Mira

772 views · 2mo ago · 1:06:43

Guest: Daniel Mira

Full episode, transcript and resources In this episode, Daniel Mira - Head of the Propulsion Technologies Group at the Barcelona Supercomputing Center (BSC) - joins Neil to explain why turbulent combustion is one of CFD's hardest problems, what makes hydrogen fundamentally different from hydrocarbon fuels, and how GPUs, exascale computing and AI could change combustion design.

Topics: Why combustion CFD is so hard · Daniel's path to jet-engine combustion · Hydrogen versus hydrocarbon fuels · When hydrogen reaches industry · How jet engines must change · Redesigning the whole aircraft

Can AI Learn Physics? Differentiable Simulation & Foundation Models | Nils Thuerey

1.1K views · 3mo ago · 1:14:48

Guest: Nils Thuerey

Full episode, transcript and resources Continue watching — Foundation Models and Scientific ML In this episode, Professor Nils Thuerey - Associate Professor at the Technical University of Munich (TUM) and head of its Physics-based Simulation group - joins Neil to discuss his journey from computational numerics and visual effects to differentiable simulation and physics-based deep learning.

Topics: Podcast intro · Introducing Prof. Nils Thuerey · Conversation begins · From computational numerics to graphics and visual effects · Physics-based deep learning before ChatGPT · CNNs, graphics and the move into engineering applications

Can AI Fix Turbulence Models? AI for Fluid Mechanics | Paola Cinnella

793 views · 3mo ago · 1:25:40

Guest: Paola Cinnella

Full episode, transcript and resources Continue watching — Turbulence Models and CFD Pioneers 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.

Topics: Podcast intro · Introducing Prof. Paola Cinnella · Conversation begins · How Paola found fluid mechanics · Moving from Italy to France · High-order schemes and compressible flows

Can AI Discover New Fluid Mechanics? | Ricardo Vinuesa

1.6K views · 4mo ago · 1:05:49

Guest: Ricardo Vinuesa

Episode and transcript Related playlist 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?

Topics: Podcast Intro · The Evolution of Foundation Models in Fluid Dynamics · Understanding Explainable AI in Fluid Mechanics · Challenges in Data Fidelity for Foundation Models · Machine Learning vs. Reduced Order Modeling · The Shift in Focus: Turbulence Modeling to Surrogate Models

Physics-Informed AI: What Actually Works? | Nathan Kutz

1K views · 4mo ago · 1:17:24

Guests: Nathan Kutz, Physics-Informed AI

Full episode, transcript and resources Continue watching — Foundation Models and Scientific ML 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.

Topics: Podcast intro · Welcoming Prof Kutz · The Evolution of Data-Driven Modeling · Understanding the SINDy Algorithm and Its Implications · Comparing Reduced Order Modeling and Modern Machine Learning · The Role of Data in Machine Learning and Physics

Agentic AI for CAE/EDA: Are Foundation Models Rewriting Engineering?

867 views · 5mo ago · 28:19

Full episode, transcript and resources Continue watching — AI for CFD & Computational 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.

Topics: Podcast intro · Historical overview: From code writing in the 60s to commercial software · Growth of aerospace and automotive industry codes and commercialization · The impact of HPC, cloud computing, and hardware evolution · Rise of cloud SaaS models and "sassification" of simulation tools · Big tech entrance: AWS, Microsoft, and Google in CAE & EDA

Fluid Intelligence: Can AI Learn the Physics of Fluids? | Brandstetter & Mishra

1.7K views · 11mo ago · 1:24:55

Guest: Fluid Intelligence

Full episode, transcript and resources Continue watching — Foundation Models and Scientific ML 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.

Topics: Background of the Participants · The Journey to the Paper · Understanding the CFD Process · The Role of AI in CFD · Exploring the Paper's Structure · Input-Output Relations in CFD

What HPC and CAE Conferences Reveal About AI in Engineering

597 views · 12mo ago · 22:38

Full episode, transcript and resources Continue watching — Engineering Careers 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.

Topics: Podcast intro · AIAA Events: A Deep Dive into Aerospace · Niche Workshops and NASA Events · Exploring Automotive Conferences · Diving into Machine Learning and Fluid Dynamics · Navigating Machine Learning Conferences

The Future of CFD: 5 Key Trends Revisited

797 views · 1y ago · 20:09

Full episode, transcript and resources Continue watching — AI for CFD & Computational 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.

Topics: The Rise of GPUs in CFD · AI and Machine Learning in CFD · The Shift to Cloud Computing · Digital Certification and High Fidelity Methods · Mergers, Acquisitions, and Innovations in CFD

Turbulence Modelling and the Origins of Modern CFD | Brian Launder

1K views · 1y ago · 1:28:24

Guest: Brian Launder

Full episode, transcript and resources Continue watching — Turbulence Models and CFD Pioneers In this episode, Professor Brian Launder (Professor at the University of Manchester and Fellow of the Royal Society and Royal Academy of Engineers) shares his remarkable journey through academia, detailing his early fascination with heat transfer, his transition to MIT, and his significant contributions to turbulence modeling and computational fluid dynamics (CFD).

Topics: Podcast intro · Early Academic Journey · Transition to MIT and Research Focus · Return to Imperial College and Early Career · Research Projects and PhD Students · Development of the k-epsilon model

Cloud HPC for Engineering: Building Rescale | CEO Joris Poort

747 views · 1y ago · 1:39:49

Guest: CEO Joris Poort

Full episode, transcript and resources Continue watching — Engineering Careers 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.

Topics: Introductions · The Genesis of Rescale: A Cloud Computing Journey · From Engineering to Entrepreneurship: The Leap of Faith · Traits of a Successful Founder: Courage and Perseverance · Tactical Steps to Startup Success: Building from the Ground Up · Milestones and Breakthroughs: The Early Days of Rescale

5 Skills CAE Engineers Need in the AI Era

1.6K views · 1y ago · 23:49

Full episode, transcript and resources Continue watching — Engineering Careers 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.

Topics: Five Tips for CAE Engineers in the Era of AI · 1: Keeping an Open Mind · 2: Educate yourself: AI, AI Physics · 3: Get ready for AI in Engineering : data and people · 4: AI Physics: The Build vs. Buy Dilemma · 5: The Future of Agentic AI in Engineering

Can We Build a Foundation Model for CFD? | Johannes Brandstetter

2.9K views · 1y ago · 1:18:03

Guest: Johannes Brandstetter

Full episode, transcript and resources Continue watching — Foundation Models and Scientific ML 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).

Topics: The Aurora Project and Key Learnings · Machine Learning in Engineering and CFD · Challenges with Mesh Graph Networks · Transformers in Physics Modeling · Tokenization in CFD with Transformers · Challenges in High-Dimensional Meshes