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Found 5 papers, 5 papers with code
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discovery
We propose a new parameter-adaptive uncertainty-penalized Bayesian information criterion (UBIC) to prioritize the parsimonious partial differential equation (PDE) that sufficiently governs noisy spatial-temporal observed data with few reliable terms.
Noise-aware Physics-informed Machine Learning for Robust PDE Discovery
This work is concerned with discovering the governing partial differential equation (PDE) of a physical system.
Adversarial Multi-task Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations
Recently, researchers have utilized neural networks to accurately solve partial differential equations (PDEs), enabling the mesh-free method for scientific computation.
Encoder-Decoder Based Convolutional Neural Network with Multi-Scale-Aware Modules for Crowd Counting
In this paper, we proposed two modified neural network architectures based on SFANet and SegNet respectively for accurate and efficient crowd counting.
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting
Inspired by SFANet, the first model, which is named M-SFANet, is attached with atrous spatial pyramid pooling (ASPP) and context-aware module (CAN).
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Crowd Counting
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