A method to scale second-order training for PINNs, based on domain decomposition and adversarial adaptive sampling.

This paper proposes an approach for split conformal prediction with unsupervised calibration samples. Theoretical and experimental results show that the presented methods can achieve performance comparable to that with supervised calibration

We theoretically characterize MoM's optimality for different classes of distributions under adversarial contamination