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#pdes

3 curated events
papersTODAY 04:00 UTC

Gauge-Aware Transport Method Extends Adaptive Meshes for Neural PDE Operators

A new arXiv preprint argues that existing adaptive-mesh methods for neural operators, which solve partial differential equations, focus mostly on deciding where to place sample points. The authors propose an approach that also addresses how the operator should interact with those points, using a gauge-aware transport formulation. The work aims to let operators adapt to local physical features in a more complete way.

neural-operators/pdes/adaptive-meshes/research
papersSEP 10 04:00 UTC

Study Examines Translation Invariance of Neural Operators on the FitzHugh-Nagumo Model

A revised arXiv paper investigates how well neural operators, a family of deep learning frameworks for approximating partial differential equation solution operators, handle stiff spatio-temporal dynamics. The work centers on the FitzHugh-Nagumo model, testing translation invariance as a key property for capturing its behavior.

neural-operators/research/pdes/scientific-computing
papersSEP 10 04:00 UTC

Gradient-guided Gaussian adaptive sampling proposed for training physics-informed neural networks

A new arXiv paper introduces 3GAS-PINNs, a variant of physics-informed neural networks that uses gradient-guided Gaussian adaptive sampling to place collocation points. The method targets common weaknesses in PINNs on nonlinear partial differential equations, such as slow convergence, gradient imbalance, and poor resolution of demanding regions. By concentrating sampling where it matters most, the approach aims to improve training efficiency and solution accuracy.

pinns/pdes/adaptive-sampling/training