mlsanityData to 7 Oct 2026

Physics Informed Neural Networks for Simulating Radiative Transfer

arXiv 2009.13291 ↗First posted 25 Sep 2020cs.LG stat.MLcited by 35

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Abstract (arXiv)

We propose a novel machine learning algorithm for simulating radiative transfer. Our algorithm is based on physics informed neural networks (PINNs), which are trained by minimizing the residual of the underlying radiative tranfer equations. We present extensive experiments and theoretical error estimates to demonstrate that PINNs provide a very easy to implement, fast, robust and accurate method for simulating radiative transfer. We also present a PINN based algorithm for simulating inverse problems for radiative transfer efficiently.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 8; 2023: 6; 2024: 6; 2025: 5; 2026: 6.

35 papers.

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  1. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  2. 2021On the approximation of functions by tanh neural networkscited by 81
  3. 2024From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learningcited by 73
  4. 2022Generic bounds on the approximation error for physics-informed (and) operator learningcited by 65
  5. 2022Error estimates for physics informed neural networks approximating the Navier-Stokes equationscited by 63
  6. 2021Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEscited by 56
  7. 2024Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?cited by 31
  8. 2022wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation lawscited by 29
  9. 2023An operator preconditioning perspective on training in physics-informed machine learningcited by 27
  10. 2022Partial Differential Equations Meet Deep Neural Networks: A Surveycited by 25
  11. 2021Machine learning moment closure models for the radiative transfer equation I: directly learning a gradient based closurecited by 8
  12. 2024Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problemcited by 8
23 more
  1. 2024Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Basescited by 7
  2. 2022Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions?cited by 6
  3. 2023Error convergence and engineering-guided hyperparameter search of PINNs: towards optimized I-FENN performancecited by 5
  4. 2021Deep neural network surrogates for non-smooth quantities of interest in shape uncertainty quantificationcited by 4
  5. 2022Physics-constrained Unsupervised Learning of Partial Differential Equations using Meshescited by 2
  6. 2022A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equationscited by 2
  7. 2025Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solverscited by 2
  8. 2023Error Analysis of Physics-Informed Neural Networks for Approximating Dynamic PDEs of Second Order in Timecited by 1
  9. 2023Maximum-likelihood Estimators in Physics-Informed Neural Networks for High-dimensional Inverse Problemscited by 1
  10. 2025Understanding Generalization in Physics Informed Models through Affine Variety Dimensionscited by 1
  11. 2026Machine learning moment closure models for the radiative transfer equation IV: enforcing symmetrizable hyperbolicity in two dimensionscited by 1
  12. 2023Number Theoretic Accelerated Learning of Physics-Informed Neural Networkscited by 0
  13. 2023Bayesian Reasoning for Physics Informed Neural Networkscited by 0
  14. 2024Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equationscited by 0
  15. 2024Error Analysis and Numerical Algorithm for PDE Approximation with Hidden-Layer Concatenated Physics Informed Neural Networkscited by 0
  16. 2025Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gasescited by 0
  17. 2025Emulating Radiative Transfer in Astrophysical Environmentscited by 0
  18. 2025Physics Enhanced Deep Surrogates for the Phonon Boltzmann Transport Equationcited by 0
  19. 2026DeepLight: A Sobolev-trained Image-to-Image Surrogate Model for Light Transport in Tissuecited by 0
  20. 2026Convolution Operator Network for Forward and Inverse Problems (FI-Conv): Application to Plasma Turbulence Simulationscited by 0
  21. 2026A Family of Adaptive Activation Functions for Mitigating Failure Modes in Physics-Informed Neural Networkscited by 0
  22. 2026Nested Fourier-enhanced neural operator for efficient modeling of radiation transfer in firescited by 0
  23. 2026INEUS: Iterative Neural Solver for High-Dimensional PIDEscited by 0