Jul 2, 2024 · ... neural-network-based PDE solvers developed following dynamic system theory. ... In this paper, we leverage the recent advances in Implicit Neural Representations ...

Jul 8, 2024 · The key objective of ML-based PDE solvers is to output a sufficiently accurate solution faster than standard numerical methods, which are used as a baseline ...

2 days ago · Graph convolution network-based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing.

Jul 3, 2024 · In this work, we investigate and extend neural PDE solvers as a tool to aid in scaling simulations for two-phase flow problems, and simulations of oil expulsion ...

Jul 1, 2024 · A thorough review of methods to facilitate real-time analysis for digital twins. •. Approaches to reduce the compute cost with machine learning and reduced ...

Jul 10, 2024 · In this work, a deep learning model based on data-driven modern koopman operator theory and dynamical system identification is proposed. The model does not ...

Jul 2, 2024 · Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, ...

Jul 16, 2024 · This review article is organised as follows: Section 2 reviews the preliminaries and notations of the graph theory and the fundamental algorithms of GNNs.

Jul 15, 2024 · The framework enables inference of time-varying potential landscapes that drive the process. The resulting forces can be interpreted as the optimal control that ...

Jul 5, 2024 · Based on the advances in representing signals with neural networks, implicit neural representations ... continual segmentation in dynamic environments. We ...