1 citations · 1 across the 6 of their papers we have counts for
8 papers
Generalised Mixing-Plane Method for Compressible Reacting-Mixture Flows in Steady Multiphysics Turbomachinery Simulations
Yifeng Wang, Lin Shi, Fenglai Huang +3
The mixing-plane method plays a pivotal role in steady simulations of multiple turbomachinery components. With advances in computational power, a whole-engine gas turbine simulatio…
A subcell-refined entropy-residual-driven limiting strategy for high-order discontinuous Galerkin methods
Geng Liang, Rui Wang, Junjie Wang +3
Fine-grained, subcell-level dissipation control is essential for achieving robust high-order discontinuous Galerkin (DG) simulations of nonlinear hyperbolic systems in under-resolv…
Identifying sensitivity-dominant parameters via active subspaces in reduced-order modeling of fluid dynamics
Dewu Yang, Rui Wang, Pengyu Lai +3
Reduced-order models (ROMs) are widely employed to describe complex system dynamics when simulations with full-order models (FOMs) are computationally prohibitive. This study prese…
From Complex Dynamics to DynFormer: Rethinking Transformers for PDEs
Pengyu Lai, Yixiao Chen, Dewu Yang +3
Partial differential equations (PDEs) are fundamental for modeling complex physical systems, yet classical numerical solvers face prohibitive computational costs in high-dimensiona…
DyMixOp: A Neural Operator Designed from a Complex Dynamics Perspective with Local-Global Mixing for Solving PDEs
Pengyu Lai, Yixiao Chen, Dewu Yang +3
A primary challenge in using neural networks to approximate nonlinear dynamical systems governed by partial differential equations (PDEs) lies in recasting these systems into a tra…
LFR-PINO: A Layered Fourier Reduced Physics-Informed Neural Operator for Parametric PDEs
Jing Wang, Biao Chen, Hairun Xie +4
Physics-informed neural operators have emerged as a powerful paradigm for solving parametric partial differential equations (PDEs), particularly in the aerospace field, enabling th…
Neural Downscaling for Complex Systems: from Large-scale to Small-scale by Neural Operator
Pengyu Lai, Jing Wang, Rui Wang +3
Predicting and understanding the chaotic dynamics in complex systems is essential in various applications. However, conventional approaches, whether full-scale simulations or small…
A Novel Paradigm in Solving Multiscale Problems
Jing Wang, Zheng Li, Pengyu Lai +5
Multiscale phenomena manifest across various scientific domains, presenting a ubiquitous challenge in accurately and effectively simulating multiscale dynamics in complex systems.…