The accurate prediction of failure boundaries in engineering applications is essential for ensuring safety and reliability. Traditional methods often rely heavily on high-fidelity physical experiments or numerical simulations, which are prohibitively expensive and time-consuming. In response to this challenge, our research proposes an innovative multi-fidelity support vector classification approach that leverages an abundant supply of low-fidelity data alongside a limited amount of high-fidelity data. This combination significantly reduces modeling costs while maintaining or even enhancing pre...
