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 ...

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

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 ...

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 ...

Overview: Clear problem definitions prevent wasted effort and keep machine learning work focused.Clean, well-understood data ...

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特征值问题，我们提出了一种求解任意阶本征值和本征态的机器学习方 ...

Artificial intelligence (AI) is increasingly transforming computational mechanics, yet many AI-driven models remain limited by poor interpretability, weak generalization, and insufficient physical ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

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 ...

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

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