Risk-averse design optimization with CVaR constraints via multifidelity tail-region correction

What the math gives to ML

The paper's transferable contribution is a tail-focused multifidelity correction strategy: use a cheap global predictor, quantify its finite-sample uncertainty, and spend expensive evaluations only near the estimated risk boundary and where the correction can materially change CVaR. This is more targeted than ordinary uncertainty sampling because it optimizes the quantity that matters—an upper quantile or tail average—rather than reducing average prediction error. A neural implementation can use a low-cost surrogate or frozen backbone, an uncertainty estimator such as an ensemble, and a regularized residual model trained only on high-fidelity discrepancies. The strongest application is expensive simulator or safety-label learning, where reducing error in rare catastrophic outcomes matters more than improving central-region MSE.