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.

Chapters

00:00 Introduction to the Podcast and Topic
01:09 Foundational Models in Fluid Dynamics
10:09 The Evolution of AI in CFD
19:52 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.

Chapters

00:00 Introduction to the Podcast and Themes
04:55 Kurt Bergin-Taylor: Background and Role at Tudor Pro Cycling
10:08 The Structure and Dynamics of a Pro Cycling Team
12:59 Innovation in Cycling: Aerodynamics, Thermal Management, and Safety
19:14 Nutrition, Training, and Performance in Cycling
29:18 Future Innovations in Cycling Equipment and Systems
30:42 Understanding Individualization in Cycling Gear
34:30 Collaborative Innovation in Cycling Equipment
38:20 The Evolving Mindset of Young Cyclists
42:28 Enhancing Viewer Engagement in Cycling
46:24 The Future of Data and AI in Cycling
50:05 Advice for Aspiring Engineers in Cycling

Takeaways

- 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.

In this episode of the Neil Ashton podcast, Neil discusses career advice for aspiring engineers, focusing on the differences between various types of companies, job roles, and the growing importance of software skills in the engineering field. The conversation highlights the pros and cons of working in large enterprises, startups, and consulting firms, as well as the diverse career paths available beyond traditional engineering roles. In this conversation, Neil discusses the evolving landscape of engineering careers, particularly focusing on the increasing relevance of software development and the tech sector. He highlights the diverse career paths available within tech, including software development, product management, and solution architecture, as well as the growing importance of AI in engineering. Neil emphasizes the opportunities for engineers to transition into tech roles and the need for a strong understanding of the tech ecosystem to navigate career decisions effectively.

Chapters

00:00 Introduction to Engineering Careers
03:01 Exploring Company Types in Engineering
06:05 Understanding Job Roles in Engineering
09:00 The Shift Towards Software in Engineering
11:52 Diverse Career Paths Beyond Traditional Engineering
14:47 The Role of Consulting in Engineering
18:03 Navigating the Job Market in Engineering
20:57 The Importance of Software Skills in Engineering
24:03 Conclusion and Future Trends in Engineering Careers
30:08 The Rise of Software Development in Engineering
31:59 The Tech Sector's Growing Relevance to Engineers
36:41 Career Paths in Tech: Software Development and Management
44:27 Understanding Product Management in Tech
48:15 The Role of Solution Architects in Tech
52:04 Consulting and Support Roles in Tech
55:54 AI's Impact on Engineering and Software Development

#careers #engineering #tech #sde #amazon #aws #google #jobs

In this episode of the Neil Ashton podcast, Professor Michael Mahoney discusses the intersection of machine learning, mathematics, and computer science. The conversation covers topics such as randomized linear algebra, foundational models for science, and the debate between physics-informed and data-driven approaches. Prof. Mahoney shares insights on the relevance of his research, the potential of using randomness in algorithms, and the evolving landscape of machine learning in scientific disciplines. He also discusses the evolution and practical applications of randomized linear algebra in machine learning, emphasizing the importance of randomness and data availability. He explores the tension between traditional scientific methods and modern machine learning approaches, highlighting the need for collaboration across disciplines. Prof Mahoney also addresses the challenges of data licensing and the commercial viability of machine learning solutions, offering insights for aspiring researchers in the field.

Prof. Mahoney website: https://www.stat.berkeley.edu/~mmahoney/
Google scholar: https://scholar.google.com/citations?user=QXyvv94AAAAJ&hl=en
Youtube version: https://youtu.be/lk4lvKQsqWU

Chapters

00:00 Introduction to the Podcast and Guest
05:51 Understanding Randomized Linear Algebra
19:09 Foundational Models for Science
32:29 Physics-Informed vs Data-Driven Approaches
38:36 The Practical Application of Randomized Linear Algebra
39:32 Creative Destruction in Linear Algebra and Machine Learning
40:32 The Role of Randomness in Scientific Machine Learning
41:56 Identifying Commonalities Across Scientific Domains
42:52 The Horizontal vs. Vertical Application of Machine Learning
44:19 The Challenge of Common Architectures in Science
46:31 Data Availability and Licensing Issues
50:04 The Future of Foundation Models in Science
54:21 The Commercial Viability of Machine Learning Solutions
58:05 Emerging Opportunities in Scientific Machine Learning
01:00:24 Navigating Academia and Industry in Machine Learning
01:11:15 Advice for Aspiring Scientific Machine Learning Researchers

Keywords

machine learning, randomized linear algebra, foundational models, physics-informed neural networks, data-driven science, computational efficiency, academic advice, numerical methods, AI in science, engineering, Randomized Linear Algebra, Machine Learning, Scientific Computing, Data Availability, Foundation Models, Academia, Industry, Research, Algorithms, Innovation

In this episode of the Neil Ashton Podcast, Dr. Prith Banerjee, CTO of Ansys, shares his extensive journey from academia to the corporate world, discussing the interplay between academia and industry, the role of startups in innovation, and the transformative potential of AI and ML in simulation. He emphasizes the importance of solving real-world problems and the need for collaboration between academia, startups, and large corporations to foster disruptive innovation. He discusses innovative business models for data sharing, the intersection of data-driven and physics-informed approaches, the role of open source in AI innovation, the potential of foundational models in computer-aided engineering (CAE), the future of quantum computing in simulation, and offers advice for aspiring innovators and entrepreneurs. He emphasizes the importance of collaboration, data governance, and the need for interdisciplinary approaches to solve complex problems in engineering and technology.

Dr. Banerjee's book - The Innovation factory: https://www.amazon.com/Innovation-Factory-Prith-Banerjee-PH/dp/B0B7LZPDZW

Youtube version of this episode: https://youtu.be/9Ic5xgJt6BQ

Chapters

00:00 Introduction to the Podcast and Guest
05:18 Dr. Prith Banerjee's Journey: From Academia to CTO
09:10 The Role of Academia, Startups, and Industry
17:22 Advice for Startups: Motivation and Market Sizing
24:04 The Impact of AI and ML on Simulation
35:07 Future of AI in Physics and Simulation
36:10 The Power of Data in AI Models
40:33 Incentivizing Data Sharing for Better Models
42:55 Physics-Driven vs Data-Driven Approaches
47:30 The Role of Open Source in AI Innovation
52:06 Foundational Models and Simulation Data
58:22 The Future of CAE and Quantum Computing
01:06:29 Advice for Aspiring Innovators

Keywords

Neil Ashton, Prith Banerjee, CAE, AI, ML, simulation, academia, startups, industry, innovation, AI, data sharing, physics-driven, open source, foundational models, quantum computing, CAE, simulation, innovation, engineering

In this episode of the Neil Ashton podcast, Peter Coen from NASA discusses the evolution and future of supersonic travel, focusing on the challenges faced by the Concorde, the technological hurdles of modern supersonic aircraft, and the innovative NASA Quesst mission (and X-59 demonstrator) that aims to provide crucial data to rewrite the aviation noise regulations. The conversation delves into the history of supersonic flight, the impact of sonic booms, and the regulatory landscape that will shape the future of aviation. In this conversation, Peter discusses the complexities of supersonic flight, focusing on the physics of shockwaves, innovative design strategies to mitigate sonic booms, and advancements in pilot visibility technology. He emphasizes the importance of human factors in aircraft design and the role of simulation in the development process. The discussion also covers the challenges of engine technology for commercial supersonic travel, the potential for hypersonic passenger travel, and the future of battery technology in aviation. Finally, Peter offers career advice for aspiring professionals in the aeronautics field.

Links
NASA Quesst mission: https://www.nasa.gov/mission/quesst/
AIAA Low-Boom Prediction Workshop: https://lbpw.larc.nasa.gov
X-59 (Lockheed Martin website): https://www.lockheedmartin.com/en-us/products/x-59-quiet-supersonic.html

Chapters

00:00 Introduction to Supersonic Travel
04:05 The History of Supersonic Flight
09:56 Challenges Faced by Concorde
16:02 Technological Challenges of Supersonic Travel
25:48 NASA's X-59 and the Quest Mission
33:45 Future of Supersonic Travel and Regulations
38:04 Understanding Shockwaves in Supersonic Flight
40:02 Design Innovations for Sonic Boom Reduction
43:16 Advancements in Pilot Visibility Technology
46:27 Human Factors in Aircraft Design
48:23 The Role of Simulation in Aircraft Development
51:42 Engine Noise and Its Impact on Supersonic Travel
54:31 The Future of Commercial Supersonic Travel
57:13 Challenges in Engine Technology for Supersonic Aircraft
01:00:17 The Intersection of Military and Supersonic Travel
01:02:09 Exploring Hypersonic Passenger Travel
01:06:39 The Future of Battery Technology in Aviation
01:09:09 Career Advice for Aspiring Aeronautics Professionals

Keywords

supersonic travel, Concorde, NASA, X-59, sonic boom, aviation technology, hypersonic flight, aerospace engineering, aircraft design, noise regulations, supersonic flight, sonic boom, aircraft design, pilot technology, simulation, engine noise, commercial aviation, hypersonic travel, battery technology, aeronautics careers, Peter Coen

In this episode of the Neil Ashton podcast, we celebrate the life and contributions of Professor Antony Jameson, a pioneer in Computational Fluid Dynamics (CFD). The conversation explores his early influences, academic journey, and significant contributions to aerodynamics and engineering. Professor Jameson shares insights from his career in both academia and industry, highlighting pivotal moments that shaped his work in CFD and transonic flow. Prof. Jameson discusses his journey through the complexities of numerical methods for fluid flow, his transition from industry to academia, the development of influential flow codes, and the evolution of computational fluid dynamics (CFD). He reflects on the challenges of teaching, the impact of his work on the aerospace industry, and the commercialization of CFD technologies. In this conversation, he shares his journey from academia to industry, discussing the challenges and successes he faced in the field of aerodynamics and computational fluid dynamics. He reflects on the importance of innovation, the impact of industry experience on academic research, and offers valuable advice for aspiring professionals in aeronautics. The discussion also touches on the evolution of computational power and the role of machine learning in the field.

Chapters

00:00 Introduction to Computational Fluid Dynamics and Professor Jameson
05:02 Professor Jameson's Early Life and Influences
20:00 Academic Journey and Contributions to Aerodynamics
34:50 Career in Industry and Transition to Academia
48:52 Pivotal Moments in Computational Fluid Dynamics
50:19 Navigating Numerical Methods for Fluid Flow
57:02 Transitioning to Academia and Teaching Challenges
01:06:25 Developing Flow Codes FLO & SYN and Their Impact
01:12:21 The Evolution of Computational Fluid Dynamics
01:19:10 Commercialization and the Future of CFD
01:30:34 Journey to Success: From Code to Commercialization
01:37:02 Innovations in Aerodynamics: Control Theory and Design
01:43:06 The Impact of Industry Experience on Academic Research
01:51:24 The Evolution of Computational Power in Aerodynamics
02:01:29 Advice for Aspiring Aeronautics Professionals

Summary of key work:

(see http://aero-comlab.stanford.edu/jameson/publication_list.html for the publication number)
Th first work that had a strong impact on the aircraft industry was Flo22. The numerical algorithm used in Flo22 is analyzed in detail in Publication 31, Iterative solution of transonic flows.
The next work that had a worldwide impact was the JST scheme in 1981. The AIAA Paper 81-1259 (publication 67) has more than 6000 citations on Google Scholar. Prof. Jameson gave two other presentations a few months earlier which describe the numerical method in more detail. These are publications 63 and 65. More recently he gave a history of the JST scheme and its further development in publication 456, which also gives a detailed discussion of the multigrid scheme which was first described in publication 78.
The Airplane Code described in AIAA Paper 86-0103 (publication 104) was the first code that could solve the Euler equations for a complete aircraft, the culmination of 15 years of his efforts to calculate transonic flows for progressively more complex configurations and with more complete mathematical models. It was never published as a journal article. The design of algorithms for unstructured grids is comprehensively discussed in his book (publication 500).
He proposed the idea of using control theory for aerodynamic shape optimization in 1988 in publication 127, and its further development for transonic flows modeled by the RANS equations is described publications 222 and 229. Its most striking application was the aerodynamic design of the Gulfstream G650 in 2006, when he performed the calculations with Syn107 on a server in his garage.