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Deep learning algorithm offers accurate SPDE solutions in high dimensions

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces a deep learning-based algorithm for approximating solutions to stochastic partial differential equations (SPDEs). This method utilizes neural networks to estimate SPDE solutions based on noise process realizations, enabling the calculation of functionals like mean and variance. The algorithm has demonstrated accuracy and efficiency in simulations involving various SPDEs, including stochastic heat and Black-Scholes equations, even in up to 100 spatial dimensions. AI

RANK_REASON The cluster contains a research paper detailing a novel algorithm for solving complex mathematical equations using deep learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning algorithm offers accurate SPDE solutions in high dimensions

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The cluster contains a research paper detailing a novel algorithm for solving complex mathematical equations using deep learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld ·

    Deep learning based numerical approximation algorithms for stochastic partial differential equations

    arXiv:2012.01194v3 Announce Type: replace-cross Abstract: In this article, we introduce a deep learning based approximation algorithm for SPDEs. Our approach employs neural networks to approximate the solutions of SPDEs along given realizations of the driving noise process. If ap…

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