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DeepONet

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Jun 19, 2026 · Artificial Intelligence

DeepONet Neural Operator for Fast Prediction of Non‑Smooth Discontinuities in the Sod Shock Tube

This tutorial presents a complete DeepONet workflow—two‑step separated training, Rowdy activation, SVD orthogonalisation, and a 10‑member ensemble—that predicts the density, velocity and pressure fields of the one‑dimensional Sod shock‑tube problem with an average test‑set relative error of 2.23% after only 22 minutes of training on an RTX 4090.

DeepONetEnsembleJAX

0 likes · 22 min read

DeepONet Neural Operator for Fast Prediction of Non‑Smooth Discontinuities in the Sod Shock Tube

AI Agent Research Hub

May 19, 2026 · Artificial Intelligence

Master Data‑Driven Neural Operators: DeepONet, POD‑DeepONet, FNO‑2D and Time‑Stepping FNO‑1D in One Tutorial

This tutorial presents a comprehensive JAX implementation and analysis of four neural‑operator methods—standard DeepONet, POD‑DeepONet, 2‑D Fourier Neural Operator, and time‑stepping 1‑D FNO—applied to the 1‑D advection equation, comparing their mathematical foundations, parameter counts, training efficiency, and prediction accuracy.

DeepONetFNOJAX

0 likes · 20 min read

Master Data‑Driven Neural Operators: DeepONet, POD‑DeepONet, FNO‑2D and Time‑Stepping FNO‑1D in One Tutorial

AI Agent Research Hub

Mar 20, 2026 · Artificial Intelligence

Spectral Division of Labor: How HINTS Blends Jacobi and DeepONet for Uniform PDE Convergence

The HINTS framework exploits the complementary spectral biases of classical Jacobi/Gauss‑Seidel relaxations and DeepONet neural operators, alternating them at a fixed ratio to achieve fast, uniform convergence for both positive‑definite and indefinite PDE systems, and integrates seamlessly with multigrid and Krylov solvers.

DeepONetHybrid Iterative MethodsJacobi

0 likes · 27 min read

Spectral Division of Labor: How HINTS Blends Jacobi and DeepONet for Uniform PDE Convergence