Integral equations and machine learning

DOI10.1016/j.matcom.2019.01.010OpenAlexW2898801573MaRDI QIDQ1997552

Ken Dahm, Alexander Keller

Publication date: 2 March 2021

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1712.06115

zbMATH Keywords

integral equationsartificial neural networksreinforcement learningMonte Carlo and quasi-Monte Carlo methodslight transport simulation

Mathematics Subject Classification ID

Numerical approximation and computational geometry (primarily algorithms) (65Dxx) Computing methodologies and applications (68Uxx)

Related Items (2)

A new approach to the numerical solution of Fredholm integral equations using least squares-support vector regression ⋮ Numerical simulation of Volterra-Fredholm integral equations using least squares support vector regression

Uses Software

• rhalton
• Noise2Noise
• Algorithm 247

Cites Work

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• Multilayer feedforward networks are universal approximators
• Learning light transport the reinforced way
• \({\mathcal Q}\)-learning
• Global illumination with radiance regression functions
• Hierarchical Monte Carlo image synthesis

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