Summary
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 www.luminarycloud.com and Prof Alonso's Stanford research at: adl.stanford.edu
Chapters
06:00 Introduction and Background
09:11 Early Interest in Aerospace Engineering
12:13 From Academia to Industry
15:11 Decision to Stay in Academia
17:11 Balancing Fundamental Science and Applied Research
22:14 Early Aims and Focus on High Performance Computing
29:18 Emergence of GPU Computing and Cloud Computing
32:23 Conditions for Innovation and Entrepreneurship
35:01 The Importance of the Bay Area
35:37 Challenges and Requirements in Developing Solvers
41:00 The Role of the Bay Area in Attracting Computational Science Talent
44:16 The Difficulty and Respect for Building High-Quality Commercial Software
47:03 The Transition of the Aerospace Industry towards Commercial Software
49:30 The Potential of Cloud Computing in Revolutionizing CFD
53:59 Progress towards the Goals of the 2030 CFD Vision Report
01:00:53 The Role of Machine Learning and AI in Simulation-Driven Workflows
01:04:01 Applications of ML and AI in Engineering
01:05:36 Optimization and Design Optimization with ML and AI
01:06:04 Outer Loops and Uncertainty Quantification
01:07:04 Digital Twin Frameworks and Constant Retraining
01:12:36 The Value of Open-Source Codes in Academia
01:16:19 Challenges of Integrating Commercial Tools with Research
01:25:20 The Future of Computational-Driven Science
01:29:01 Continued Innovation and Replacement of Physical Experimentation