The Intersection of AI and Physics in Advanced Manufacturing | Robin Toluie, PhysicsX
Release Date: 01/30/2025
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info_outlineIn this episode of the Tesoro AI podcast, we sit with Robin Toluie, a key figure behind PhysicsX, a company leveraging AI and machine learning to accelerate simulation and design processes across various advanced industries. They delve into Robin's extensive background in both theoretical physics and high-stakes automotive engineering with leading companies like Mercedes AMG and Bentley Motors. They discuss the application of machine learning in optimizing vehicle designs, reducing costs through virtual simulations, and its broader implications beyond automotive—applying to aerospace, renewables, and medical fields. Robin highlights PhysicsX’s innovative use of large physics models and deep learning to replace traditional numerical simulations, driving faster and more efficient product development. They also explore the potential and limitations of AI-generated simulations, the role of agents in automating engineering workflows, and the exciting developments upcoming in the field, including the release of Airplane 2.0.
If your company is looking to scale its AI initiatives, head over to Tesoro AI (www.tesoroai.com). We are experts in AI strategy, staff augmentation, and AI product development.
Founder Bio:
Robin Tuluie is the founder and vice chairman of PhysicsX, pioneering deep learning and simulation solutions for breakthrough engineering. A theoretical physicist, Robin discovered a new effect in cosmic background radiation, later confirmed by the Nobel-winning COBE satellite. Transitioning to Formula One, he led R&D at Renault (Alpine) F1, securing back-to-back championships, then at Mercedes F1, helping build one of the sport’s most dominant teams. After further success at Bentley Motors, Ducati MotoGP, and Volkswagen Group, he founded PhysicsX in 2019 to revolutionize engineering with AI-driven simulation, tackling climate and industrial challenges.
Time Stamps:
00:33 Robin's Journey and Background
06:15 Transition to Physics X
09:42 Challenges in Generative AI and Physics
12:29 Talent and Team at Physics X
14:17 Comparison Between Simulations
19:53 Trust and Validation in AI Models
22:24 Understanding Large Physics Models
24:20 Introducing AI.Rplane: AI-Powered Design Tool
26:04 Future of AI Agents in Engineering
31:58 Applications in Manufacturing and Operations
35:51 3D Printing and Design Optimization
37:16 User Experience and Team Structure
40:17 Global Team and Future Prospects
45:24 Conclusion and Future Releases
Resources
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