Data-driven Optimization using Deep Reinforcement Learning with Applications in Semiconductor Testing and Digital Healthcare

Beschreibung

The growing availability of data in semiconductor testing and digital healthcare creates new opportunities for data-driven optimization. At the same time, applications in these domains are highly performance-critical and must satisfy strict constraints. General-purpose optimization methods are rarely tailored to these settings, which often makes them computationally expensive and unsuitable for real-time operation. This thesis investigates data-driven optimization strategies that are explicitly trained for domain-specific requirements and designed for fast time to optimize using Deep Reinforcement Learning.

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Peter Domanski

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Data-driven Optimization Semiconductor Testing Digital Healthcare

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