Deep set based operator learning with uncertainty quantification

ArXi:2509.25646v2 Announce Type: replace Learning operators from data is central to scientific machine learning. While DeepONets are widely used for their ability to handle complex domains, they require fixed sensor numbers and locations, lack mechanisms for uncertainty quantification, and are thus limited in practical applicability.