Active learning of deep surrogates for PDEs: Application to metasurface design. - Raphaël Pestourie; Youssef Mroueh; Thanh V. Nguyen; Payel Das; Steven G. Johnson (CoRR, 2020)

Physics-enhanced deep surrogates for PDEs. - Raphaël Pestourie; Youssef Mroueh; Chris Rackauckas; Payel Das; Steven G. Johnson (CoRR, 2021)

Physics-Informed Neural Networks with Hard Constraints for Inverse Design. - Lu Lu 010; Raphaël Pestourie; Wenjie Yao; Zhicheng Wang; Francesc Verdugo; Steven G. Johnson (SIAM J. Sci. Comput., 2021)

Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport. - Lu Lu 010; Raphaël Pestourie; Steven G. Johnson; Giuseppe Romano (CoRR, 2022)

Inverse-Designed Meta-Optics with Spectral-Spatial Engineered Response to Mimic Color Perception. - Chris Munley; Wenchao Ma; Johannes E. Fröch; Quentin A. A. Tanguy; Elyas Bayati; Karl F. Böhringer; Zin Lin; Raphaël Pestourie; Steven G. Johnson; Arka Majumdar (CoRR, 2022)

Physics-enhanced deep surrogates for partial differential equations. - Raphaël Pestourie; Youssef Mroueh; Chris Rackauckas; Payel Das; Steven G. Johnson (Nat. Mac. Intell., 2023)

HiLAB: A Hybrid Inverse-Design Framework. - Reza Marzban; Hamed Abiri; Raphaël Pestourie; Ali Adibi (CoRR, 2025)

Inverse Design in Nanophotonics via Representation Learning. - Reza Marzban; Ali Adibi; Raphaël Pestourie (CoRR, 2025)