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Fourier spectral differentiation offers significant speedup for physics-informed neural networks

By PulseAugur Editorial · [1 sources] ·

A new research paper compares two methods for calculating spatial derivatives in physics-informed neural networks (PINNs): automatic differentiation (AD) and Fourier spectral differentiation. The study found that Fourier spectral differentiation significantly reduces training time and memory usage, with speedups ranging from 2.90x to 18.52x and memory reductions of 68.7% to 94.1%. The accuracy of the solutions remained comparable between the two methods, suggesting Fourier spectral differentiation is a more efficient approach for periodic PINNs on uniform grids. AI

IMPACT This research offers a more efficient method for training physics-informed neural networks, potentially accelerating scientific discovery in fields relying on differential equations.

RANK_REASON The cluster contains an academic paper detailing a new computational method for improving the efficiency of physics-informed neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Fourier spectral differentiation offers significant speedup for physics-informed neural networks

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The cluster contains an academic paper detailing a new computational method for improving the efficiency of physics-informed neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xilai Liang, Zhao Zhang ·

    A Computational Comparison of Fourier Spectral Differentiation and Spatial Automatic Differentiation in Periodic Physics-Informed Neural Networks

    arXiv:2609.02110v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) commonly evaluate the spatial derivatives appearing in partial differential equation residuals using automatic differentiation (AD), whose computational and memory costs can become substantia…

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