4 papers
LegONet: Plug-and-Play Structure-Preserving Neural Operator Blocks for Compositional PDE Learning
Jiahao Zhang, Yueqi Wang, Guang Lin
Learned PDE solvers are often trained as monolithic surrogates for a specific equation, boundary condition and discretization. This makes them difficult to reuse when mechanisms ch…
A POD-DeepONet Framework for Forward and Inverse Design of 2D Photonic Crystals
Yueqi Wang, Guanglian Li, Guang Lin
We develop a reduced-order operator-learning framework for forward and inverse band-structure design of two-dimensional photonic crystals with binary, pixel-based -symmetric u…
Reduced-Basis Deep Operator Learning for Parametric PDEs with Independently Varying Boundary and Source Data
Yueqi Wang, Guang Lin
Parametric PDEs power modern simulation, design, and digital-twin systems, yet their many-query workloads still hinge on repeatedly solving large finite-element systems. Existing o…
Generative Prior-Guided Neural Interface Reconstruction for 3D Electrical Impedance Tomography
Haibo Liu, Junqing Chen, Guang Lin
Reconstructing complex 3D interfaces from indirect measurements remains a grand challenge in scientific computing, particularly for ill-posed inverse problems like Electrical Imped…