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machine learning for transport simulation. A core innovation involves Bayesian metamodeling techniques to construct fast surrogate models of the simulation space, enabling efficient scenario analysis
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across the value chain. Using Bayesian Optimization / Modern Design of Experiment, we build the data-foundation to enable true hybrid development between humans and advanced learning algorithms such as
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