郭义销，明平兵，于灏高维薛定谔特征值问题在诸多科学和工程领域中起着至关重要的作用。然而，由于维数灾难和奇异势函数等困难，精确求解这一问题面临巨大挑战。因此，为该问题设计高精度的高效计算方法具有重要意义。针对高维区域上薛定谔算子的Dirichlet特征 ...

Recent advances at the intersection of neural networks and inverse scattering problems have transformed traditional approaches to imaging and material characterisation. Inverse scattering involves ...

Neural networks have emerged as a powerful framework for addressing complex problems across numerous scientific domains. In particular, the interplay between neural network models and constraint ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...

The target article (Pater 2019) proposes to use neural networks to model learning within existing grammatical frameworks. This is easier said than done. There is a fundamental gap to be bridged that ...

Article reviewed by Grace Lindsay, PhD from New York University. Scientists design ANNs to function like neurons. 6 They write lines of code in an algorithm such that there are nodes that each contain ...

Past psychology and behavioral science studies have identified various ways in which people's acquisition of new knowledge ...

Even networks long considered "untrainable" can learn effectively with a bit of a helping hand. Researchers at MIT's Computer ...

Many "AI experts" have sprung up in the machine learning space since the advent of ChatGPT and other advanced generative AI constructs late last year, but Dr. James McCaffrey of Microsoft Research is ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

As the name suggests, neural networks are inspired by the brain. A neural network is designed to mimic how our brains work to ...