3 papers
math.OC2026
A Backstepping Framework for Unconstrained Accelerated Optimization Algorithms
Song Chen, Jiaxu Liu, Chao Xu
This paper introduces a control-theoretic perspective on unconstrained optimization algorithms using the backstepping methods. We model the optimization process as an augmented str…
cs.LG2026
Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction
Yixiao Qian, Jiaxu Liu, Zewei Xia +3
Physics-Informed Neural Networks (PINNs) offer a powerful paradigm for flow reconstruction, seamlessly integrating sparse velocity measurements with the governing Navier-Stokes equ…
cs.CE2025
An Efficient Graph-Transformer Operator for Learning Physical Dynamics with Manifolds Embedding
Pengwei Liu, Xingyu Ren, Pengkai Wang +6
Accurate and efficient physical simulations are essential in science and engineering, yet traditional numerical solvers face significant challenges in computational cost when handl…