Uploaded May 2026 | Updated September 2026, 3 weeks ago
Recorded 22 May 2026. Shancong Mou of the University of Minnesota, Twin Cities, presents "Derivative-Informed Training of Neural Operators on the Fly" at IPAM's Multi-Fidelity Methods to Enable Robust Optimization and Real-Time Control of Fusion Processes Workshop.
Abstract: Recently, training deep neural operators with derivative information—often using offline-generated derivative pairs—has shown clear benefits in both the pretraining stage and downstream PDE-constrained optimization problems. In this talk, I will discuss recent developments in on-the-fly derivative-informed training, including how to generate and incorporate derivative information during training, what important lessons we have learned, and how these insights can be used to further improve downstream PDE-constrained optimization.
Learn more online at: https://www.ipam.ucla.edu/programs/workshops/workshop-iv-multi-fidelity-methods-to-enable-robust-optimization-and-real-time-control-of-fusion-processes/?tab=overview
Recorded 22 May 2026. Shancong Mou of the University of Minnesota, Twin Cities, presents "Derivative-Informed Training of Neural Operators on the Fly" at IPAM's Multi-Fidelity Methods to Enable Robust Optimization and Real-Time Control of Fusion Processes Workshop.
Abstract: Recently, training deep neural operators with derivative information—often using offline-generated derivative pairs—has shown clear benefits in both the pretraining stage and downstream PDE-constrained optimization problems. In this talk, I will discuss recent developments in on-the-fly derivative-informed training, including how to generate and incorporate derivative information during training, what important lessons we have learned, and how these insights can be used to further improve downstream PDE-constrained optimization.
Learn more online at: https://www.ipam.ucla.edu/programs/workshops/workshop-iv-multi-fidelity-methods-to-enable-robust-optimization-and-real-time-control-of-fusion-processes/?tab=overview
