Artificial intelligence has significantly advanced by integrating biological principles, such as evolution, into machine learning models. Evolutionary algorithms, inspired by natural selection and genetic mutation, are commonly used to optimize compl...
Self-organized criticality and mass extinction in evolutionary algorithms The gaps in the fossil record gave rise to the hypothesis that evolution proceeded in long periods of stasis, which alternated with occasional, rapid chang... T Krink,ET Ren - Congress on Evolutionary Computation 被引量: 87...
Knowledge diffusion simulation of knowledge networks: based on complex network evolutionary algorithmsdoi:10.1007/s10586-018-2559-3Li ZhangQifeng WeiYuan YuanYuxue LiSpringer US
Inverse design methods based on optimization algorithms, such as evolutionary algorithms, and topological optimizations, have been introduced to design metamaterials. However, none of these algorithms are general enough to fulfill multi-objective tasks. Recently, deep learning methods represented by ...
Physics-informed neural networks (NN) are an emerging technique to improve spatial resolution and enforce physical consistency of data from physics models or satellite observations. A super-resolution (SR) technique is explored to reconstruct high-resolution images ( 4imes 4imes ) from lower resolutio...
Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023). Article CAS PubMed Google Scholar Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: new docking methods, expanded force field, and ...
The crystal diffusion variational autoencoder (CDVAE) is a machine learning model that leverages score matching to generate realistic crystal structures that preserve crystal symmetry. In this study, we leverage novel diffusion probabilistic (DP) models
(MAR) techniques are crucial for improving image quality by mitigating these artifacts. Various MAR methods have been proposed [1,2,3], including iteration-based [4] and projection correction [5,6] algorithms. Recently, deep learning (DL) methods have emerged as effective tools in MAR ...
data-driven algorithms. Most of these approaches have, however, been restricted to linear material properties such as the effective elastic stiffness in three dimensions7,8or Poisson’s ratio9. Extensions to nonlinearity (for example, via multi-material configurations) have been presented recently10but...
. As such, future research with GCDM could involve adding new time-efficient graph construction or sampling algorithms55or exploring the impact of higher-order (e.g., type-2 tensor) yet efficient geometric expressiveness56on 3D generative models to accelerate sample generation and increase sample ...