complex geometries 1design automation 1fluid dynamics 1graph optimization 1milp optimization 1multi-agent systems 1multi-scale methods 1physics-informed neural networks 1reduced-order modeling 1weak formulation 1zero-dimensional models 1
From the 2 of 3 linked papers with an AI index.
3 papers
cs.LG2026
MUSA-PINN: Multi-scale Weak-form Physics-Informed Neural Networks for Fluid Flow in Complex Geometries
Weizheng Zhang, Xunjie Xie, Hao Pan +4
The paper introduces MUSA-PINN, a multi-scale weak-form physics-informed neural network that enforces integral conservation laws over hierarchical spherical control volumes to impr…
cs.LG2026
A Multi-Agent Framework for Zero-Dimensional Reduced-Order Model Planning
Bingteng Sun, Hao Yin, Yiling Chen +9
The paper introduces Z-COPA, a multi‑agent framework that uses a symbolic graph engine and MILP‑guided optimization to automate the planning of zero‑dimensional reduced‑order model…
physics.flu-dyn2026
Flow Field Reconstruction via Voronoi-Enhanced Physics-Informed Neural Networks with End-to-End Sensor Placement Optimization
Renjie Xiao, Bingteng Sun, Yiling Chen +3
(short version abstract, full in article)High-fidelity flow field reconstruction is important in fluid dynamics, but it is challenged by sparse and spatiotemporally incomplete sens…