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Date
2020-09Type
- Report
ETH Bibliography
yes
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Abstract
We propose a novel machine learning algorithm for simulating radiative transfer. Our algorithmis based on physics informed neural networks (PINNs), which are trained by minimizing the residualof the underlying radiative tranfer equations. We present extensive experiments and theoretical errorestimates to demonstrate that PINNs provide a very easy to implement, fast, robust and accuratemethod for simulating radiative transfer. We also present a PINN based algorithm for simulatinginverse problems for radiative transfer efficiently. Show more
Publication status
publishedExternal links
Journal / series
SAM Research ReportVolume
Publisher
Seminar for Applied Mathematics, ETH ZurichSubject
Deep Learning; Physic Informed; Neural Networks; Radiative TransferOrganisational unit
03851 - Mishra, Siddhartha / Mishra, Siddhartha
Related publications and datasets
Is part of: https://doi.org/10.3929/ethz-b-000488139
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ETH Bibliography
yes
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