Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

The use and creation of machine‐learning‐based solutions to solve problems or reduce
their computational costs are becoming increasingly widespread in many domains. Deep …

Inspired by the great success of machine learning (ML), researchers have applied ML
techniques to visualizations to achieve a better design, development, and evaluation of …

With increasing amounts of information, the image information received by people also
increases exponentially. To perform fine-grained categorization and recognition of images …

We propose GNN-Surrogate, a graph neural network-based surrogate model to explore the
parameter space of ocean climate simulations. Parameter space exploration is important for …

Inspired by the great success of machine learning (ML), researchers have applied ML
techniques to visualizations to achieve a better design, development, and evaluation of …

Computational models, such as simulations, are central to a wide range of fields in science
and industry. Those models take input parameters and produce some output. To fully exploit …

摘要随着人工智能技术的突破性进展, 人工智能与可视化的交叉研究成为当前的研究热点之一,
为人工智能和大数据分析领域的若干核心难题提供了启发式的理论, 方法和技术. 一方面 …

Numerical simulations are commonly used to understand the parameter dependence of
given spatio-temporal phenomena. Sampling a multi-dimensional parameter space and …

We propose VDL-Surrogate, a view-dependent neural-network-latent-based surrogate
model for parameter space exploration of ensemble simulations that allows high-resolution …