| Abstract: |
| Deep learning surrogate models have shown significant promise in solving partial differential equations. These efficient models enable many-query computations in science and engineering, with particular focus on engineering design optimization, which is the central topic of this talk. I will begin by introducing the neural operator approach for surrogate modeling, followed by a theoretical analysis of Bayesian nonparametric regression of linear functionals to better understand the sample complexity. |
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