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Learn to bridge the gap between ModelingToolkit.jl's acausal modeling capabilities and JuMP.jl's optimization solvers through an innovative abstraction layer in this 15-minute conference talk. Discover how to automatically translate complex physics-based engineering models from ModelingToolkit.jl into JuMP constraints, enabling access to a broader range of optimization solvers including deterministic global optimizers. Explore the motivation behind this work, particularly the need for guaranteed global optimality in nonconvex engineering applications where economic and safety considerations are paramount. See a practical demonstration of constructing nonlinear, nonconvex models using ModelingToolkit, transforming them into standard JuMP models, and solving them to global optimality using the EAGO solver without requiring modifications to the optimizer itself. Understand how this open-source abstraction layer provides engineers with the flexibility to optimize any ModelingToolkit model using their preferred standardized solver, combining the benefits of acausal modeling with the extensive optimization ecosystem available through JuMP.
