搜索结果: 1-15 共查到“neural”相关记录23条 . 查询时间(0.287 秒)
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：VC-PINN: variable coefficient physics-informed neural network for forward and inverse problems of PDEs with variable coefficient
VC-PINN 变系数 偏微分方程 正逆问题 变系数物理 知情神经网络
2023/11/13
The talk proposes a deep learning method specifically dealing with the forward and inverse problem of variable coefficient partial differential equations-Variable Coefficient Physics-Informed Neural N...
中国科学院半导体研究所高速电路与神经网络实验室刘婧逸博士一篇论文被人工智能顶刊Neural Networks录用（图）
中国科学院 高速电路 神经网络 刘婧逸 人工智能 Neural Networks
2023/11/28
近日，实验室2021级博士研究生刘婧逸的学术论文《SNR: Symbolic Network-based Rectifiable Learning Framework for Symbolic Regression》被人工智能顶级期刊Neural Networks录用。《Neural Networks》是世界上三个最古老的神经建模学会的档案期刊:国际神经网络学会(INNS)、欧洲神经网络学会(EN...
Cornell researchers have developed an optical neural network (ONN) that can filter relevant information from a scene before the visual image is detected by a camera, a method that may make it possible...
In the vast, expansive skies where birds once ruled supreme, a new crop of aviators is taking flight. These pioneers of the air are not living creatures, but rather a product of deliberate innovation:...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Entropy-Dissipation Informed Neural Networks for McKean-Vlasov type PDEs
McKean-Vlasov型 偏微分方程 熵耗散 信息神经网络
2023/4/13
We extend the concept of self-consistency for the Fokker-Planck equation (FPE) [Shen et al., 2022] to the more general McKean-Vlasov equation (MVE). While FPE describes the macroscopic behavior of par...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Barron Type Spaces in Neural Network Approximation
神经网络 巴伦类型空间 经典函数空间
2023/4/13
We shall discuss various Barron type spaces arising from neural networks. The relations among these spaces will be clarified, and we shall also establish the relationship between Barron type spaces an...
A method for designing neural networks optimally suited for certain tasks（图）
神经网络 机器学习模型 计算机网络
2023/6/20
Neural networks, a type of machine-learning model, are being used to help humans complete a wide variety of tasks, from predicting if someone’s credit score is high enough to qualify for a loan to dia...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Deep neural network approximation to inverse conductivity problems for elliptic equations
椭圆方程 反电导率 深度神经网络
2023/4/14
The focus of this talk is on the numerical methods used to identify parameters in partial differential equations. Typically, an optimization approach is used to solve this class of inverse problems, w...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Generalization of Graph Neural Networks and Graph Structural Learning for Robust Representation
图神经网络 泛化 图结构学习 鲁棒表示
2023/4/18
This report consists of two parts associated with graph neural networks: generalization and graph structural learning. We first study the Rademacher complexity of GNNs, as one of independent-algorithm...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Topology optimization via implicit neural representations
隐式 神经表示 拓扑优化
2023/4/24
Along with the rapid development of artificial intelligence (AI) technology, scientific research enters a new era of AI. Topology optimization (TO) and AI technology are recently showing a growing tre...
Sparse, small, but diverse neural connections help make perception reliable, efficient（图）
神经连接 丘脑输入 视觉皮层神经元 脑科学
2023/6/5
The brain’s cerebral cortex produces perception based on the sensory information it’s fed through a region called the thalamus.
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Implicit biases of optimization algorithms for neural networks and their effects on generalization
神经网络 优化算法 隐式偏差 泛化的影响
2023/4/26
Modern neural networks are usually over-parameterized—the number of parameters exceeds the number of training data. In this case the loss functions tend to have many (or even infinite) global minima, ...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Convergence Thery of Deep Neural Networks: Arbitrary Activation Functions and Pooling
深度神经网络 激活函数 池化
2023/4/28
Deep neural networks, as a powerful system to represent high dimensional complex functions, play a key role in deep learning. Convergence of deep neural networks is a fundamental issue in building the...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions
守恒量 两阶段物理信息 神经网络方法 局部波解
2023/5/5
With the advantages of fast calculating speed and high precision, the physics-informed neural network method opens up a new approach for numerically solving nonlinear partial differential equations. B...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Calculation of high-order harmonic generation of atoms and molecules by combining time series prediction and neural networks
时间序列预测 神经网络计算 原子分子 高阶谐波生成
2023/5/9
High-order harmonic generation (HHG) from the interaction of ultra-intense laser pulses with atoms is an important tabletop short-wave coherent light source. Accurate quantum simulations of it present...