This article reviews applications of Bayesian inference and machine learning (ML) in
nuclear fusion research. Current and next-generation nuclear fusion experiments require …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Neural approximations of scalar-and vector fields, such as signed distance functions and
radiance fields, have emerged as accurate, high-quality representations. State-of-the-art …

Most neural compression models are trained on large datasets of images or videos in order
to generalize to unseen data. Such generalization typically requires large and expressive …

Neural compression algorithms are typically based on autoencoders that require specialized
encoder and decoder architectures for different data modalities. In this paper, we propose …

High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional
subspace. Engineering applications for modeling, characterization, design, and control of …

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 …

Although deep learning has demonstrated its capability in solving diverse scientific
visualization problems, it still lacks generalization power across different tasks. To address …

Error-bounded lossy compression has been identified as a promising solution for
significantly reducing scientific data volumes upon users' requirements on data distortion …

Despite recent advances in implicit neural representations (INRs), it remains challenging for
a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representation …