Triply-Scalable Equivariant Gaussian Process Modeling

This paper addresses the computational cost limitations of Gaussian Processes in large-scale scientific problems. The proposed method achieves triply-scalable performance by combining symmetry encoding, approximation techniques, and efficient inference, enabling GPs to handle high-dimensional inputs and large symmetry groups while preserving probabilistic predictions and uncertainty quantification that are valuable in molecular research applications.