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cs.LG2026
Neural Operators for Multi-Task Control and Adaptation
David Sewell, Xingjian Li, Stepan Tretiakov +2
Neural operator methods have emerged as powerful tools for learning mappings between infinite-dimensional function spaces, yet their potential in optimal control remains largely un…
cs.LG2025
Learning Generalizable Neural Operators for Inverse Problems
Adam J. Thorpe, Stepan Tretiakov, Dibakar Roy Sarkar +2
Inverse problems challenge existing neural operator architectures because ill-posed inverse maps violate continuity, uniqueness, and stability assumptions. We introduce B2B${}^{-1}…
cs.LG2025
SetONet: A Set-Based Operator Network for Solving PDEs with Variable-Input Sampling
Stepan Tretiakov, Xingjian Li, Krishna Kumar
Most neural-operator surrogates for PDEs inherit from DeepONet-style formulations the requirement that the input function be sampled at a fixed, ordered set of sensors. This assump…