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Analysis, Algorithms, and Applications of Neural Networks

Organizer(s):
Name:	Affiliation:	Country:
Juncai He	King Abdullah University of Science and Technology	Saudi Arabia
Xinliang Liu	King Abdullah University of Science and Technology	Saudi Arabia
Jinchao Xu	King Abdullah University of Science and Technology	Saudi Arabia

Introduction:

In the rapidly evolving landscape of neural network applications, including convolutional, recurrent, graph networks, and Transformers, the understanding of these NN-based models has become critically important in the fields of computational and applied mathematics. This special session at AIMS 2024 aims to provide a platform for researchers and practitioners to share insights, methodologies, and breakthroughs focused on the mathematical and numerical analysis, efficient algorithm developments, and explainable applications of these models and methods. The session will primarily feature contributions from junior professors and young researchers from various universities and countries, fostering a collaborative and dynamic exchange of ideas.

All Abstracts

List of abstracts and speakers
Name	Affiliation	Title
Yongqiang Cai	Beijing Normal University	Neural network, dynamical system and formal language
Ruchi Guo	Sichuan University	Structure-conforming Operator Learning for Geometric Inverse Problems
Juncai He	The King Abdullah University of Science and Technology	Neural Networks and Operators Based on Convolution and Multigrid Structure
Boou Jiang	The King Abdullah University of Science and Technology	DualFL-CS: an accelerated, inexact, and parallel coordinate descent method for federated learning
Pengzhan Jin	Peking University	A deformation-based framework for learning solution mappings of PDEs defined on varying domains
Yuwen Li	Zhejiang University	Entropy-based convergence rates of greedy algorithms
Tong Mao	King Abdullah University of Science and Technology	Expressivity and Approximation Properties of Deep Neural Networks with ReLUk Activation
Fei Wang	Xi`an Jiaotong University	Adaptive Growing Randomized Neural Networks for Solving Partial Differential Equations
Yaoyu Zhang	Shanghai Jiao Tong University	Optimistic Sample Size Estimate for Deep Neural Networks