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20222026
most citedMultifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport

2 citations · 2 across the 3 of their papers we have counts for

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physics.comp-ph2025

Physics Enhanced Deep Surrogates for the Phonon Boltzmann Transport Equation

Antonio Varagnolo, Giuseppe Romano, Raphaël Pestourie

Designing materials with controlled heat flow at the nano-scale is central to advances in microelectronics, thermoelectrics, and energy-conversion technologies. At these scales, ph…

physics.comp-ph2022★ 2 cited

Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport

Lu Lu, Raphael Pestourie, Steven G. Johnson +1

Deep neural operators can learn operators mapping between infinite-dimensional function spaces via deep neural networks and have become an emerging paradigm of scientific machine l…