The Neil Ashton Podcast

• S2, EP8 - Neil Ashton - Career advice for Engineers

  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.Chapters00:00 Introduction to Engineering Careers03:01 Exploring Company Types in Engineering06:05 Understanding Job Roles in Engineering09:00 The Shift Towards Software in Engineering11:52 Diverse Career Paths Beyond Traditional Engineering14:47 The Role of Consulting in Engineering18:03 Navigating the Job Market in Engineering20:57 The Importance of Software Skills in Engineering24:03 Conclusion and Future Trends in Engineering Careers30:08 The Rise of Software Development in Engineering31:59 The Tech Sector's Growing Relevance to Engineers36:41 Career Paths in Tech: Software Development and Management44:27 Understanding Product Management in Tech48:15 The Role of Solution Architects in Tech52:04 Consulting and Support Roles in Tech55:54 AI's Impact on Engineering and Software Development#careers #engineering #tech #sde #amazon #aws #google #jobs

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• S2, EP7 - Prof. Michael Mahoney - Perspectives on AI4Science

  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=enYoutube version: https://youtu.be/lk4lvKQsqWUChapters00:00 Introduction to the Podcast and Guest05:51 Understanding Randomized Linear Algebra19:09 Foundational Models for Science32:29 Physics-Informed vs Data-Driven Approaches38:36 The Practical Application of Randomized Linear Algebra39:32 Creative Destruction in Linear Algebra and Machine Learning40:32 The Role of Randomness in Scientific Machine Learning41:56 Identifying Commonalities Across Scientific Domains42:52 The Horizontal vs. Vertical Application of Machine Learning44:19 The Challenge of Common Architectures in Science46:31 Data Availability and Licensing Issues50:04 The Future of Foundation Models in Science54:21 The Commercial Viability of Machine Learning Solutions58:05 Emerging Opportunities in Scientific Machine Learning01:00:24 Navigating Academia and Industry in Machine Learning01:11:15 Advice for Aspiring Scientific Machine Learning ResearchersKeywordsmachine 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

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• S2, EP6 - Dr. Prith Banerjee - ANSYS CTO

  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/B0B7LZPDZWYoutube version of this episode: https://youtu.be/9Ic5xgJt6BQChapters00:00 Introduction to the Podcast and Guest05:18 Dr. Prith Banerjee's Journey: From Academia to CTO09:10 The Role of Academia, Startups, and Industry17:22 Advice for Startups: Motivation and Market Sizing24:04 The Impact of AI and ML on Simulation35:07 Future of AI in Physics and Simulation36:10 The Power of Data in AI Models40:33 Incentivizing Data Sharing for Better Models42:55 Physics-Driven vs Data-Driven Approaches47:30 The Role of Open Source in AI Innovation52:06 Foundational Models and Simulation Data58:22 The Future of CAE and Quantum Computing01:06:29 Advice for Aspiring InnovatorsKeywordsNeil 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

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• S2, EP5 - NASA's Quesst for Quieter Supersonic Flight with Peter Coen

  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.LinksNASA Quesst mission: https://www.nasa.gov/mission/quesst/AIAA Low-Boom Prediction Workshop: https://lbpw.larc.nasa.govX-59 (Lockheed Martin website): https://www.lockheedmartin.com/en-us/products/x-59-quiet-supersonic.htmlChapters00:00 Introduction to Supersonic Travel04:05 The History of Supersonic Flight09:56 Challenges Faced by Concorde16:02 Technological Challenges of Supersonic Travel25:48 NASA's X-59 and the Quest Mission33:45 Future of Supersonic Travel and Regulations38:04 Understanding Shockwaves in Supersonic Flight40:02 Design Innovations for Sonic Boom Reduction43:16 Advancements in Pilot Visibility Technology46:27 Human Factors in Aircraft Design48:23 The Role of Simulation in Aircraft Development51:42 Engine Noise and Its Impact on Supersonic Travel54:31 The Future of Commercial Supersonic Travel57:13 Challenges in Engine Technology for Supersonic Aircraft01:00:17 The Intersection of Military and Supersonic Travel01:02:09 Exploring Hypersonic Passenger Travel01:06:39 The Future of Battery Technology in Aviation01:09:09 Career Advice for Aspiring Aeronautics ProfessionalsKeywordssupersonic 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

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• S2, EP4 - Celebrating Prof. Antony Jameson: A CFD Pioneer

  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.Chapters00:00 Introduction to Computational Fluid Dynamics and Professor Jameson05:02 Professor Jameson's Early Life and Influences20:00 Academic Journey and Contributions to Aerodynamics34:50 Career in Industry and Transition to Academia48:52 Pivotal Moments in Computational Fluid Dynamics50:19 Navigating Numerical Methods for Fluid Flow57:02 Transitioning to Academia and Teaching Challenges01:06:25 Developing Flow Codes FLO & SYN and Their Impact01:12:21 The Evolution of Computational Fluid Dynamics01:19:10 Commercialization and the Future of CFD01:30:34 Journey to Success: From Code to Commercialization01:37:02 Innovations in Aerodynamics: Control Theory and Design01:43:06 The Impact of Industry Experience on Academic Research01:51:24 The Evolution of Computational Power in Aerodynamics02:01:29 Advice for Aspiring Aeronautics ProfessionalsSummary 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.

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