The Neil Ashton Podcast

In this episode of the Neil Ashton podcast, Nikolas Tombazis discusses his journey into engineering and Formula One, starting from his passion for mathematics, physics, and design. He shares how his childhood dream of designing Formula One cars led him to pursue engineering. Tombazis also talks about his experience at Cambridge University and the freedom he enjoyed during his university years. He then delves into his decision to pursue a PhD in experimental aerodynamics and the valuable skills he gained from his research. Tombazis reflects on the challenges and responsibilities of being a chief aerodynamicist in Formula One, as well as the evolving role of CFD in the industry. The conversation explores the advancements in wind tunnel technology and computational fluid dynamics (CFD) in Formula One. It discusses the role of CFD as a design tool and the potential for it to become the predominant tool in the future. The conversation also touches on the balance between the technical aspects of the sport and the entertainment value for fans. The importance of teamwork, leadership, and culture in Formula One teams is highlighted, as well as the challenges of maintaining success and avoiding complacency. The conversation concludes with advice for aspiring Formula One professionals, emphasizing the value of a broad skill set and the potential for Formula One as a stepping stone to other industries.Chapters00:00 Introduction to the Podcast and Season Two03:38 Nikolas Tombazis: A Key Figure in Formula One04:56 Early Influences and Passion for Engineering08:52 The Journey Through Cambridge and PhD Studies12:57 Entering Formula One: The Path to Benetton18:25 The Evolution of Aerodynamics in Formula One24:06 The Role of CFD and Wind Tunnel Technology38:53 Balancing Technology and Entertainment in F144:47 The Future of AI in Formula One54:56 Understanding Team Dynamics and Performance Variability01:03:44 Advice for Aspiring Engineers in Formula One

The first season of the Neil Ashton podcast comes to a close with a recap of the episodes and a glimpse into what's to come in the next season. Look out for Season 2 in September with lots more great guests and discussion on hypersonics, CFD, Formula One, cycling,  space exploration and more!

Anima Anandkumar, a Bren Professor at Caltech and a leading scientist in AI and ML, shares her journey from academia to industry roles at Nvidia and AWS. They discuss the groundbreaking role of AI in enhancing weather forecasting and complex phenomena modeling, including applications in fluid dynamics and nuclear fusion. Anandkumar emphasizes the need for collaboration between AI experts and domain specialists. She also highlights the revolutionary potential of neural operators in improving simulation accuracy, bridging the gap between traditional methods and AI advancements.

Prof. Karthik Duraisamy is a Professor at the University of Michigan, the Director of the Michigan Institute for Computational Discovery and Engineering (MICDE) and the founder of the startup Geminus.AI. In this episode, we discusses AI4Science, with a particular focus on fluid dynamics and computational fluid dynamics. Prof. Duraisamy talks about the progress and challenges of using machine learning in turbulence modeling and the potential of surrogate models (both data-driven and physics-informed neural networks). He also explores the concept of foundational models for science and the role of data and physics in AI applications. The discussion highlights the importance of using machine learning as a tool in the scientific process and the potential benefits of large language models in scientific discovery. We also discuss the need for collaboration between academia, tech companies, and startups to achieve the vision of a new platform for scientific discovery. Prof. Duraisamy predicts that in the next few years, there may be major advancements in foundation models for science however he cautions against unrealistic expectations and emphasizes the importance of understanding the limitations of AI.Links:Summer school tutorials https://github.com/scifm/summer-school-2024 (scroll down for links to specific tutorials)SciFM24 recordings : https://micde.umich.edu/news-events/annual-symposia/2024-symposium/SciFM24 Summary : https://drive.google.com/file/d/1eC2HJdpfyZZ42RaT9KakcuACEo4nqAsJ/viewTrillion parameter consortium  : https://tpc.devTurbulence Modelling in the age of data: https://www.annualreviews.org/content/journals/10.1146/annurev-fluid-010518-040547LinkedIn:  https://www.linkedin.com/showcase/micde/Chapters00:00 Introduction09:41 Turbulence Modeling and Machine Learning21:30 Surrogate Models and Physics-Informed Neural Networks28:42 Foundational Models for Science35:23 The Power of Large Language Models47:43 Tools for Foundation Models48:39 Interfacing with Specialized Agents53:31 The Importance of Collaboration58:57 The Role of Agents and Solvers01:08:26 Balancing AI and Existing Expertise01:21:28 Predicting the Future of AI in Fluid Dynamics01:23:18 Closing Gaps in Turbulence Modeling01:25:42 Achieving Productivity Benefits with Existing ToolsTakeaways-Machine learning is a valuable tool in the development of turbulence modeling and other scientific applications.-Data-driven modeling can provide additional insights and improve the accuracy of scientific models.-Physics-informed neural networks have potential in solving inverse problems but may not be as effective in solving complex PDEs.-Foundational models for science can benefit from a combination of data-driven approaches and physics-based knowledge.-Large language models have the potential to assist in scientific discovery and provide valuable insights in various scientific domains. Having a strong foundation in the domain of study is crucial before applying AI techniques.-Collaboration between academia, tech companies, and startups is necessary to achieve the vision of a new platform for scientific discovery.-Understanding the limitations of AI and managing expectations is important.-AI can be a valuable tool for productivity gains and scientific assistance, but it will not replace human expertise.Keywords#computationalfluiddynamics , #ailearning #largelanguagemodels , #cfd , #supercomputing , #fluiddynamics

In this episode, Neil interviews Professor Max Welling, one of the foremost experts in Machine Learning about AI4Science: the use of machine learning and AI to solve challenges in various scientific disciplines. They discuss and debate between data-driven and physics-driven approaches, the potential for foundational models, the importance of open sourcing models and data, the challenges of data sharing in science, and the ethical considerations of releasing powerful models. The conversation covers the role of academia, industry, and startups in driving innovation, with a focus on the field of AI. Professor Welling discusses the advantages and limitations of each sector and shares his experience in academia, big tech companies, and startups. The conversation then shifts to Professor Wellings  new company; CuspAI, which focuses on material discovery for carbon capture using metal organic frameworks and machine learning. Prof. Welling provides insights into the potential applications of this technology and the importance of addressing sustainability challenges. The conversation concludes with a discussion on career advice and the future of AI for science.Links CuspAI : https://www.cusp.ai University website: https://staff.fnwi.uva.nl/m.welling/Google scholar: https://scholar.google.com/citations?user=8200InoAAAAJ&hl=enAI4Science NeurIPS 2023 workshop: https://neurips.cc/virtual/2023/workshop/66548 AI4Science NeurIPS 2022 workshop: https://nips.cc/virtual/2022/workshop/50019Aurora paper: https://arxiv.org/abs/2405.13063 Chapters00:00 Introduction to the Neil Ashton Podcast00:39 Guest Introduction: Professor Max Welling11:12 Data-Driven vs. Physics-Driven Approaches in Machine Learning for Science17:00 Foundational models for science23:08 Discussion around Open-Sourcing Models and Data29:26 Ethical Considerations in Releasing Powerful Models for Public Use33:14 Collaboration and Shared Resources in Addressing Global Challenges34:07 The Role of Academia, Industry, and Startups43:27 Material Discovery for Carbon Capture52:02 Career Advice for Early-stage Researchers01:01:07 The Future of AI for Science and SustainabilityKeywordsAI for science, machine learning, data-driven approaches, physics-driven approaches, foundational models, open sourcing, data sharing, ethical considerations, blockchain technology, academia, industry, startups, AI, material discovery, carbon capture, metal organic frameworks, machine learning, sustainability, career advice, future of AI for science

This episode sets the scene for upcoming discussions on AI4Science with world renowned experts on machine learning. The focus is on using machine learning to solve scientific problems, such as computational fluid dynamics, weather modeling, material design, and drug discovery. The episode introduces the concept of machine learning and its potential to accelerate simulations and predictions. The episode also discusses the differences between machine learning for scientific problems and large language models, and the ongoing debate on incorporating physics into machine learning models.Chapters00:30 Introduction: AI for Science and Machine Learning02:29 The Importance of Computational Fluid Dynamics04:53 The Limitations of Physical Testing and Simulation05:53 Accelerating Simulations and Predictions with Machine Learning09:51 Data-Driven vs Physics-Informed Approaches in Machine Learning13:10 The Future of Machine Learning in Science: Foundational Models

In this episode of the Neil Ashton podcast, Neil interviews Dr. Chris Rumsey,  Research Scientist at NASA Langley Research Center.  Chris is one of the main CFD experts at NASA Langley is globally reconised as a leader in CFD, particularly for aeronautical applications. The conversation focuses on computational fluid dynamics (CFD) and turbulence modeling. They discuss Chris's career, his role in public dissemination of CFD methods, and his involvement in the Turbulence Modeling website. They also explore the High Lift Prediction Workshop and the role of machine learning in CFD and turbulence modeling. The conversation provides insights into working at NASA and the challenges and advancements in CFD and turbulence modeling. In this conversation, Neil and Chris Rumsey discuss the progress and challenges in solving the problem of high-lift aerodynamics in aircraft design. They explore the concept of certification by analysis and the role of computational fluid dynamics (CFD) in reducing the need for expensive wind tunnel and flight tests. They also delve into the use of machine learning in CFD and the challenges of reproducibility. The conversation then shifts to conferences, with Neil and Chris sharing their experiences and favorite events. They conclude by discussing career advice for aspiring aerospace professionals and the unique aspects of working at NASA.00:00 Introduction to the Neil Ashton podcast01:09 Focus on Computational Fluid Dynamics and Turbulence Modeling06:51 Chris Rumsey's Journey to NASA09:13 From Art to Aeronautical Engineering13:08 Transitioning to Turbulence Modeling15:34 The Origins of the Turbulence Modeling Website20:40 Verification and Validation in Turbulence Modeling24:34 The Role of Machine Learning in Turbulence Modeling26:00 Advancements in High Lift Prediction27:28 Challenges in High Lift Prediction28:25 Thoughts on Working at NASA29:42 Certification by Analysis: Reducing the Cost of Aircraft Certification31:09 The Role of Machine Learning in CFD and Certification by Analysis34:03 The Value of Conferences in Networking and Specialized Learning40:30 Career Advice for Aspiring Aerospace Professionals48:45 Curating and Documenting Knowledge in the Aerospace Community

In this episode, Neil speaks to Professor Jack Dongarra, a renowned figure in the supercomputing and high-performance computing (HPC) world. He is a Professor at University of Tennessee as well as a Distinguished Researcher at Oak Ridge National Laboratory (ORNL) and a Turing Fellow at the University of Manchester. He is the inventor of the LINPACK library that is still used today to benchmark the Top 500 list of the most powerful supercomputers and was one of the key people involved in the creation of Message-Passing-Inferface (MPI). They discuss what is HPC, the challenges and opportunities in the field, and the future of HPC. They also touch on the role of machine learning and AI in HPC, the competitiveness of the United States in the field, and potential future technologies in HPC. Professor Dongarra shares his insights and advice based on his extensive experience in the field.As part of their discussion they discuss two papers from Prof Dongarra:1) High-Performance Computing: Challenges and Opportunities: https://arxiv.org/abs/2203.02544 2) Can the United States Maintain Its Leadership in High-Performance Computing? - A report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR Office: https://www.osti.gov/biblio/1989107/Chapters00:00 Introduction 04:18 Defining HPC and its Impact08:11 Challenges and Opportunities in HPC28:20 The Competitiveness of the United States in HPC44:31 The Future of HPC: Technologies and Innovations49:30 Insights and Advice from Professor Jack Dongarra

In this episode, Neil interviews Pat Symonds, one of the most well known and respected engineers in Formula One. They discuss Pat's career in engineering, his time in Formula One, and the evolution of the sport. Pat shares insights into his early motivations, his work with different teams, and the challenges he faced. They also touch on the growth of Motorsport Valley in the UK and the potential for Formula One teams to be based in other countries. In this conversation, Pat discusses his experience in Formula One and the challenges of being a technical director. He emphasizes the importance of continuous learning and the ability to make compromises in order to achieve success. He shares insights into the culture at Williams and Benetton and how it impacted their success. Additionally, he discusses the future of Formula One, including the use of AI and ML, the potential shift towards sustainable fuels, and the role of motor manufacturers.

In this episode I speak to Prof Juan J. Alonso on his vision of the future of computational science as well as his journey from academia to entrepreneurship - founding Luminary Cloud. He reflects on the revolutions in computational science and the different ways of developing software throughout his career. Alonso emphasizes the importance of academia in creating and perpetuating knowledge, as well as the value of innovation and new ideas. He also discusses the changes in the CFD world, the emergence of new technologies like GPU computing and cloud computing, and the potential for advancements in computational simulations for analysis and design.  We also touch on the transition of the aerospace industry towards commercial software and the potential for cloud computing to revolutionize CFD. The conversation concludes with a discussion on the progress made towards achieving the goals outlined in the 2030 CFD vision report and the role of machine learning and AI in simulation-driven workflows. In this final part of the conversation, Juan discusses the potential applications of ML and AI in engineering. He identifies four main areas where these technologies can be beneficial, but emphasizes that these applications will always be based on high-fidelity simulations.  He concludes by envisioning the future of computational-driven science and the continued innovation in the field.You can check out Luminary Cloud at https://www.luminarycloud.com and Prof Alonso's Stanford research at: https://adl.stanford.edu 06:00 Introduction and Background09:11 Early Interest in Aerospace Engineering12:13 From Academia to Industry15:11 Decision to Stay in Academia17:11 Balancing Fundamental Science and Applied Research22:14 Early Aims and Focus on High Performance Computing29:18 Emergence of GPU Computing and Cloud Computing32:23 Conditions for Innovation and Entrepreneurship35:01 The Importance of the Bay Area35:37 Challenges and Requirements in Developing Solvers41:00 The Role of the Bay Area in Attracting Computational Science Talent44:16 The Difficulty and Respect for Building High-Quality Commercial Software47:03 The Transition of the Aerospace Industry towards Commercial Software49:30 The Potential of Cloud Computing in Revolutionizing CFD53:59 Progress towards the Goals of the 2030 CFD Vision Report01:00:53 The Role of Machine Learning and AI in Simulation-Driven Workflows01:04:01 Applications of ML and AI in Engineering01:05:36 Optimization and Design Optimization with ML and AI01:06:04 Outer Loops and Uncertainty Quantification01:07:04 Digital Twin Frameworks and Constant Retraining01:12:36 The Value of Open-Source Codes in Academia01:16:19 Challenges of Integrating Commercial Tools with Research01:25:20 The Future of Computational-Driven Science01:29:01 Continued Innovation and Replacement of Physical Experimentation