Uploaded January 2026 | Updated September 2026, 2 weeks ago

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Explore how Wolfram System Modeler can serve as a powerful training ground for AI. This session showcases three cutting-edge workflows that combine physics-based modeling with machine learning. First, see how reinforcement learning agents can be trained in realistic simulated environments. Next, learn how high-fidelity models can be used to generate data for neural surrogate models—enabling faster optimization. Finally, discover how neural network–based model predictive controllers can be designed to control complex physical systems. Whether your goal is faster simulations, smarter controllers or more efficient AI training, see how System Modeler bridges the gap between engineering models and intelligent algorithms.