PyTorch Automatic Differentiation Explained for PINNs

This paper details how PyTorch's automatic differentiation engine calculates gradients for physics-informed neural networks (PINNs). It traces the entire process, from the forward pass's computational graph to the reverse-mode backward traversal, using a specific multilayer perceptron and an initial value problem. The authors verify each adjoint value against manual derivations, connecting PyTorch's autograd engine to the P/Q sensitivity framework. AI

IMPACT Provides a detailed technical explanation of gradient computation for PINNs, relevant for researchers and developers working with PyTorch.

RANK_REASON Academic paper detailing a technical aspect of automatic differentiation in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• multilayer perceptron
• physics-informed neural networks
• PyTorch
• Tahimí

• paper
• infra

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