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Solving 1D Diffusion Equation with PINN: A Practice of Physics-Informed Neural Networks from Forward to Inverse Problems

A project using Physics-Informed Neural Networks (PINN) to solve both forward and inverse problems of the 1D diffusion equation, achieving an estimation error of 0.17% for the diffusion coefficient under sparse observation data with only 1% noise.

物理信息神经网络PINN扩散方程反问题参数估计TensorFlow科学机器学习偏微分方程
Published 2026-06-16 07:43Recent activity 2026-06-16 07:48Estimated read 1 min
Solving 1D Diffusion Equation with PINN: A Practice of Physics-Informed Neural Networks from Forward to Inverse Problems
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Section 01

导读 / 主楼:Solving 1D Diffusion Equation with PINN: A Practice of Physics-Informed Neural Networks from Forward to Inverse Problems

Introduction / Main Post: Solving 1D Diffusion Equation with PINN: A Practice of Physics-Informed Neural Networks from Forward to Inverse Problems

A project using Physics-Informed Neural Networks (PINN) to solve both forward and inverse problems of the 1D diffusion equation, achieving an estimation error of 0.17% for the diffusion coefficient under sparse observation data with only 1% noise.