Engineering design optimization faces significant challenges when dealing with computationally expensive simulations (ranging from 10 minutes to several days per evaluation) and inherent uncertainties in operating conditions and manufacturing tolerances. Traditional deterministic optimization approaches often yield designs that are sensitive to these uncertainties, leading to performance degradation or constraint violations in real-world conditions.
The need to balance multiple objectives while satisfying probabilistic constraints, such as reliabil-ity requirements, is paramount in industries like aerospace, automotive, and energy. This intern-ship focuses on enhancing state-of-the-art robust optimization methodologies to address multi-objective problems under uncertainties, with a particular emphasis on handling low-probability events and accelerating the optimization process using advanced deep learning techniques.
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