Building reliable physical systems is too slow, too expensive, and too uncertain. Mainstream AI can’t solve this challenge alone because it is fundamentally ungrounded in physics. Meanwhile, traditional simulation and testing workflows can no longer keep pace with the growing complexity of modern robotics, vehicles, energy systems, and industrial automation. To break this bottleneck, companies must combine AI and high‑fidelity simulation into a unified development loop that enables co-design, accelerates development, improves prediction quality, reduces physical testing burdens, and builds trustworthy, physically grounded intelligence. Analog Devices, Inc. (ADI), CoreWeave, Monolith, and Weights & Biases each address these challenges from different but complementary angles, united by a shared belief that the future of innovation in physical systems lies at the intersection of physics‑based simulation, domain‑specific AI, and rigorous model governance. Join this webinar to learn how these approaches come together to transform the way complex physical systems are designed, tested, and deployed.
What to expect
Technical leaders from ADI, Monolith, CoreWeave, and Weights and Biases share insights and strategies on how to build effective end-to-end physical AI workflows, from optimized infrastructure and hardware design to multimodel experimentation, visualization, and simulation-driven development for reliable real-world performance.
What you’ll learn
How to co-design hardware, simulation environments, and model architectures that enable rapid iteration and robust deployment of embodied AI systems in real-world conditions.
Understand how simulated data accelerates early design iteration, while physical measurements refine and improve simulation fidelity over time, helping teams close the Sim‑to‑Real gap.
Strategies to take physical AI systems from prototype to production.
Who should attend
AI/robotics researchers and scientists, mechanical and electrical engineers, AI engineers, software developers, and AI leaders.
Webinar
