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Found 8 papers, 0 papers with code
A PNP ion channel deep learning solver with local neural network and finite element input data
Since the input data of the neural network scheme only involves a small local patch of coarse grid solutions, which the finite element solver can quickly produce, the PNPic deep learning solver can be trained much faster than any corresponding conventional global neural network solvers.
Neural Network with Local Converging Input (NNLCI) for Supersonic Flow Problems with Unstructured Grids
In the present study, we develop a neural network with local converging input (NNLCI) for high-fidelity prediction using unstructured data.
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold
Although Deep Learning (DL) has achieved success in complex Artificial Intelligence (AI) tasks, it suffers from various notorious problems (e. g., feature redundancy, and vanishing or exploding gradients), since updating parameters in Euclidean space cannot fully exploit the geometric structure of the solution space.
Solving Maxwell's Equation in 2D with Neural Networks with Local Converging Inputs
In contrast with existing research work on applying neural networks to directly solve PDEs, our method takes advantage of the local domain of dependence of the Maxwell's equation in the input solution patches, and is therefore simpler, yet still robust.
SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding
SparGE measures similarity by jointly sparse coding and graph embedding.
O-ViT: Orthogonal Vision Transformer
Inspired by the tremendous success of the self-attention mechanism in natural language processing, the Vision Transformer (ViT) creatively applies it to image patch sequences and achieves incredible performance.
Dense Multiscale Feature Fusion Pyramid Networks for Object Detection in UAV-Captured Images
Although much significant progress has been made in the research field of object detection with deep learning, there still exists a challenging task for the objects with small size, which is notably pronounced in UAV-captured images.
IDENT: Identifying Differential Equations with Numerical Time evolution
The new algorithm, called Identifying Differential Equations with Numerical Time evolution (IDENT), is explored for data with non-periodic boundary conditions, noisy data and PDEs with varying coefficients.
Numerical Analysis
