18 citations · 29 across the 4 of their papers we have counts for
4 papers
A Unified Multiscale Auxiliary PINN Framework for Generalized Phonon Transport
Roberto Riganti, Luca Dal Negro
Nanoscale thermal transport is governed by the phonon Boltzmann transport equation (BTE). However, simulating the sub-continuum dynamics remains computationally prohibitive due to…
DDNet: A Unified Physics-Informed Deep Learning Framework for Semiconductor Device Modeling
Roberto Riganti, Matteo G. C. Alasio, Enrico Bellotti +1
The accurate modeling of semiconductor devices plays a critical role in the development of new technology nodes and next-generation devices. Semiconductor device designers largely…
Multiscale Physics-Informed Neural Networks for the Inverse Design of Hyperuniform Optical Materials
Roberto Riganti, Yilin Zhu, Wei Cai +2
In this article, we employ multiscale physics-informed neural networks (MscalePINNs) for the inverse design of finite-size photonic materials with stealthy hyperuniform (SHU) disor…
Auxiliary Physics-Informed Neural Networks for Forward, Inverse, and Coupled Radiative Transfer Problems
Roberto Riganti, Luca Dal Negro
In this paper, we develop and employ auxiliary physics-informed neural networks (APINNs) to solve forward, inverse, and coupled integro-differential problems of radiative transfer…