Graph representation learning and its applications: a survey
Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …
representation learning is a significant task since it could facilitate various downstream …
[HTML][HTML] Generative ai for visualization: State of the art and future directions
Generative AI (GenAI) has witnessed remarkable progress in recent years and
demonstrated impressive performance in various generation tasks in different domains such …
demonstrated impressive performance in various generation tasks in different domains such …
A hierarchical spatial transformer for massive point samples in continuous space
Transformers are widely used deep learning architectures. Existing transformers are mostly
designed for sequences (texts or time series), images or videos, and graphs. This paper …
designed for sequences (texts or time series), images or videos, and graphs. This paper …
Dl4scivis: A state-of-the-art survey on deep learning for scientific visualization
Since 2016, we have witnessed the tremendous growth of artificial intelligence+
visualization (AI+ VIS) research. However, existing survey articles on AI+ VIS focus on visual …
visualization (AI+ VIS) research. However, existing survey articles on AI+ VIS focus on visual …
[HTML][HTML] Efficient deep data assimilation with sparse observations and time-varying sensors
Abstract Variational Data Assimilation (DA) has been broadly used in engineering problems
for field reconstruction and prediction by performing a weighted combination of multiple …
for field reconstruction and prediction by performing a weighted combination of multiple …
Learning CO2 plume migration in faulted reservoirs with Graph Neural Networks
Deep-learning-based surrogate models provide an efficient complement to numerical
simulations for subsurface flow problems such as CO 2 geological storage. Accurately …
simulations for subsurface flow problems such as CO 2 geological storage. Accurately …
A review of graph neural network applications in mechanics-related domains
Mechanics-related tasks often present unique challenges in achieving accurate geometric
and physical representations, particularly for non-uniform structures. Graph neural networks …
and physical representations, particularly for non-uniform structures. Graph neural networks …
KD-INR: Time-varying volumetric data compression via knowledge distillation-based implicit neural representation
Traditional deep learning algorithms assume that all data is available during training, which
presents challenges when handling large-scale time-varying data. To address this issue, we …
presents challenges when handling large-scale time-varying data. To address this issue, we …
[HTML][HTML] Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint
Despite the success of various methods in addressing the issue of spatial reconstruction of
dynamical systems with sparse observations, spatio-temporal prediction for sparse fields …
dynamical systems with sparse observations, spatio-temporal prediction for sparse fields …
Deep Learning for Spatiotemporal Big Data: A Vision on Opportunities and Challenges
Z Jiang - arXiv preprint arXiv:2310.19957, 2023 - arxiv.org
With advancements in GPS, remote sensing, and computational simulation, an enormous
volume of spatiotemporal data is being collected at an increasing speed from various …
volume of spatiotemporal data is being collected at an increasing speed from various …