Advertise on podcast: The Neil Ashton Podcast

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/