Fast and accurate airflow simulations in the built environment are critical to provide
acceptable thermal comfort and air quality to the occupants. Computational Fluid Dynamics …

As a form of clean energy, nuclear energy has unique advantages compared to other energy
sources in the present era, where low-carbon policies are being widely advocated. The …

The classical development of neural networks has primarily focused on learning mappings
between finite dimensional Euclidean spaces or finite sets. We propose a generalization of …

Numerical simulation of parametrized differential equations is of crucial importance in the
study of real-world phenomena in applied science and engineering. Computational methods …

Graph-based next-step prediction models have recently been very successful in modeling
complex high-dimensional physical systems on irregular meshes. However, due to their …

Traditional data-driven deep learning models often struggle with high training costs, error
accumulation, and poor generalizability in complex physical processes. Physics-informed …

The present work proposes a framework for nonlinear model order reduction based on a
Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) …

Training neural networks sequentially in time to approximate solution fields of time-
dependent partial differential equations can be beneficial for preserving causality and other …

When evaluating quantities of interest that depend on the solutions to differential equations,
we inevitably face the trade-off between accuracy and efficiency. Especially for …

Inspired by our previous work on a quadratic approximation manifold [1], we propose in this
paper a computationally tractable approach for combining a projection-based reduced-order …