Generalization Limits of In-Context Operator Networks for Higher-Order Partial Differential Equations

ArXi:2603.21534v1 Announce Type: new We investigate the generalization capabilities of In-Context Operator Networks (ICONs), a new class of operator networks that build on the principles of in-context learning, for higher-order partial differential equations. We extend previous work by expanding the type and scope of differential equations handled by the foundation model. We nstrate that while processing complex inputs requires some new computational methods, the underlying machine learning techniques are largely consistent with simpler cases.