The Neil Ashton Podcast

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). We touch upon the key role that Professor Brian Spalding had on his career as well as work that led to the breakthrough k-epilson turbulence model as well as the pioneering work on second-moment closure model. Prof Launder highlights the key role of collaborators and ex students such as Professors Hector Iacovides, Tim Craft, Bill Jones, Kemal Hanjalić and many more. He ends with advice for early-stage researchers and reflections on more than 50 years worth of academic research.Chapters00:30 Introduction05:00 Early Academic Journey10:06 Transition to MIT and Research Focus16:21 Return to Imperial College and Early Career21:06 Research Projects and PhD Students27:46 Development of the k-epilson model33:18 CHAM and Career Changes36:24 Move to UC Davis and New Research Directions44:05 Challenges and Opportunities in Research47:07 The Interview Experience51:14 Transition to Manchester University52:23 Research Innovations in Turbulence Modeling57:45 The Development of the TCL Model01:03:15 Nonlinear Eddy Viscosity Models01:05:58 Advanced Wall Functions and Their Applications01:10:09 Reflections on Career and Contributions01:15:49 Legacy and Impact on Turbulence ModelingTop Turbulence Modelling contributions (https://scholar.google.com/citations?user=Y3JbAK8AAAAJ&hl=en) 

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.Chapters00:00 Introductions03:30 The Genesis of Rescale: A Cloud Computing Journey05:21 From Engineering to Entrepreneurship: The Leap of Faith09:28 Traits of a Successful Founder: Courage and Perseverance14:51 Tactical Steps to Startup Success: Building from the Ground Up22:10 Milestones and Breakthroughs: The Early Days of Rescale30:54 Navigating Challenges: The Role of Cloud Providers in HPC35:24 The Intersection of HPC and AI Training37:05 Cloud vs On-Premise: The Cost Debate39:54 Complexities of HPC in Enterprises42:27 The Slow Shift to Cloud Adoption44:34 Optimizing Workloads with Rescale46:50 Usability Challenges in Enterprise Software48:32 The Rise of Neo Clouds and Competition51:18 Speed and Efficiency in AI Training54:34 AI's Transformative Impact on Engineering58:54 The Future of AI Surrogates in Design01:03:28 Agentic AI: The New Paradigm in Engineering01:14:21 Solving Real Business Problems01:19:26 The Impact of AI on Engineering01:22:27 Innovation in Aerospace and Beyond01:25:19 Cultural Change in Organizations01:28:34 The Future of AI and Engineering01:39:09 Advice for Aspiring FoundersKeywordsHPC, cloud computing, startup journey, Rescale, entrepreneurship, AI, technology, innovation, engineering, business, AI, engineering, inference, simulation, automation, digital twin, innovation, aerospace, machine learning, technology

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.Chapters00:00 Introduction to the Podcast and AI in Engineering01:03 Five Tips for CAE Engineers in the Era of A101:24 1: Keeping an Open Mind 07:39 2: Understanding AI Physics and Its Applications13:30 3: Preparing for AI Implementation in Engineering18:54 4: The Build vs. Buy Dilemma in AI Solutions22:20 5: The Future of Agentic AI in Engineering

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.ioEmmi AI: https://www.emmi.aiGoogle scholar: https://scholar.google.com/citations?user=KiRvOHcAAAAJ&hl=deAB-UPT transform paper: https://arxiv.org/abs/2502.09692Chapters00:00 Introduction to Johannes Brandstetter07:10 The Aurora Project and Key Learnings11:15 Machine Learning in Engineering and CFD17:19 Challenges with Mesh Graph Networks20:16 Transformers in Physics Modeling31:14 Tokenization in CFD with Transformers39:58 Challenges in High-Dimensional Meshes41:08 Inference Time and Mesh Generation41:36 Neural Operators and CAD Geometry45:59 Anchor Tokens and Scaling in CFD48:40 Data Dependency and Multi-Fidelity Models50:32 The Role of Physics in Machine Learning54:28 Temporal Modeling in Engineering Simulations56:58 Learning from Temporal Dynamics1:00:58 Stability in Rollout Predictions1:03:48 Multidisciplinary Approaches in Engineering1:05:18 The Startup Journey and Lessons Learned

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.LinksAerodynamics for engineers: https://www.cambridge.org/us/universitypress/subjects/engineering/aerospace-engineering/aerodynamics-engineers-7th-edition?format=HB&isbn=9781009501309RAeS 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 Chapters00:00 Introduction to the Podcast and Guest04:56 Professor Russell Cummings: A Journey Through Engineering31:14 The Evolution of Hypersonics Research58:26 The Role of AI in Hypersonics and CFD01:37:55 Advice for Aspiring Engineers

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/Chapters00:00 Introduction 06:25 Professor Mike Giles: A Journey Through CFD and Finance17:30 Early Academic Influences and Career Path29:34 Transition to MIT and Early Research40:01 High-Performance Computing and Its Impact41:30 Navigating Between MIT and Rolls-Royce44:54 The Evolution of Research at MIT48:47 Transitioning to Oxford and the Role of Rolls-Royce51:07 The Genesis of the Hydra Code01:00:47 The Role of Conferences in Engineering01:10:58 The Shift from CFD to Financial Applications01:21:30 Navigating Burnout and Career Transitions01:24:04 Shifting Focus: From Hydrocode to Computational Finance01:29:30 Bridging Mathematics and Finance: Methodologies and Techniques01:35:09 The Role of High-Performance Computing in Modern Research01:39:20 AI's Impact on Research and Future Directions01:54:00 Advice for the Next Generation: Pursuing Passion and Skills

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.Chapters00:00 Introduction to the Podcast and Topic01:09 Foundational Models in Fluid Dynamics10:09 The Evolution of AI in CFD19:52 Future Predictions and Industry Dynamics

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.Chapters00:00 Introduction to the Podcast and Themes04:55 Kurt Bergin-Taylor: Background and Role at Tudor Pro Cycling10:08 The Structure and Dynamics of a Pro Cycling Team12:59 Innovation in Cycling: Aerodynamics, Thermal Management, and Safety19:14 Nutrition, Training, and Performance in Cycling29:18 Future Innovations in Cycling Equipment and Systems30:42 Understanding Individualization in Cycling Gear34:30 Collaborative Innovation in Cycling Equipment38:20 The Evolving Mindset of Young Cyclists42:28 Enhancing Viewer Engagement in Cycling46:24 The Future of Data and AI in Cycling50:05 Advice for Aspiring Engineers in CyclingTakeaways- 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.

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. 

Discover valuable career advice tailored for aspiring engineers. The discussion dives into the pros and cons of different workplaces, from large corporations to startups. There's a strong emphasis on the growing role of software skills in engineering, with insights into diverse career paths like software development and solution architecture. Neil highlights the shift towards tech, AI's impact, and the importance of understanding the tech landscape to make informed career decisions. A must-listen for anyone looking to navigate the evolving engineering job market!