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New TGSR-PINN method enhances physics-informed neural network transfer learning

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called Target-Guided Selective Reweighting PINN (TGSR-PINN) to improve the transfer learning capabilities of physics-informed neural networks (PINNs) for inverse problems. This approach addresses challenges like negative transfer, where models trained on one set of physical parameters perform poorly on another due to differing mechanisms or noise. TGSR-PINN uses a target-evidence-driven strategy to correct representations by scoring neurons and applying selective soft decay to weights and biases of underperforming neurons. Experiments show this method enhances parameter recovery while maintaining accuracy in various complex physics tasks, including advection-diffusion and cross-PDE-family transfers. AI

IMPACT Improves transfer learning for physics-informed neural networks, potentially enabling more accurate parameter recovery in complex scientific simulations.

RANK_REASON The cluster contains a research paper detailing a novel method for physics-informed neural networks.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New TGSR-PINN method enhances physics-informed neural network transfer learning

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Qian Hu, Bin Fan, Yao Xiao, Zhicheng Lin, Meixin Xiong ·

    Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach

    arXiv:2607.05271v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) encounter ill-posed optimization, loss competition, and parameter compensation in partial differential equation (PDE) inverse problems. Transfer learning can reuse representations from source…

  2. arXiv cs.LG TIER_1 English(EN) · Meixin Xiong ·

    Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach

    Physics-informed neural networks (PINNs) encounter ill-posed optimization, loss competition, and parameter compensation in partial differential equation (PDE) inverse problems. Transfer learning can reuse representations from source tasks, but direct fine-tuning may introduce neg…

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