4 papers
Guiding diffusion models to reconstruct flow fields from sparse data
Marc Amorós-Trepat, Luis Medrano-Navarro, Qiang Liu +2
The reconstruction of unsteady flow fields from limited measurements is a challenging and crucial task for many engineering applications. Machine learning models are gaining popula…
Physics vs Distributions: Pareto Optimal Flow Matching with Physics Constraints
Giacomo Baldan, Qiang Liu, Alberto Guardone +1
Physics-constrained generative modeling aims to produce high-dimensional samples that are both physically consistent and distributionally accurate, a task that remains challenging…
PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations
Benjamin Holzschuh, Qiang Liu, Georg Kohl +1
We introduce PDE-Transformer, an improved transformer-based architecture for surrogate modeling of physics simulations on regular grids. We combine recent architectural improvement…
A Deep Neural Network Framework for Solving Forward and Inverse Problems in Delay Differential Equations
Housen Wang, Yuxing Chen, Sirong Cao +2
We propose a unified framework for delay differential equations (DDEs) based on deep neural networks (DNNs) - the neural delay differential equations (NDDEs), aimed at solving the…