activity
20232026
most citedMIND: Microstructure INverse Design with Generative Hybrid Neural Representation

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.GR2026

SLIM: Learning an Operator for Material-Efficient 3D Lattice Refinement

Yueze Zhu, Yu Xing, Tianyang Xue +4

Lattice inverse-design methods can generate cells that match prescribed effective properties, but property feasibility does not imply material efficiency. Removing redundant materi…

cs.GR2026

GMT: A Geometric Multigrid Transformer Solver for Microstructure Homogenization

Yu Xing, Yang Liu, Tianyang Xue +1

Lattice metamaterials enable lightweight, multifunctional structures, yet homogenization-based evaluation of their effective properties remains computationally expensive. Neural su…

cs.AI2026

AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design

Zhenyuan Zhao, Yu Xing, Tianyang Xue +3

Designing microstructures with coupled cross-physics objectives is a fundamental challenge where traditional topology optimization is often computationally prohibitive and deep gen…

cs.GR2025

MeshMosaic: Scaling Artist Mesh Generation via Local-to-Global Assembly

Rui Xu, Tianyang Xue, Qiujie Dong +9

Scaling artist-designed meshes to high triangle numbers remains challenging for autoregressive generative models. Existing transformer-based methods suffer from long-sequence bottl…

cs.CV2025★ 1 cited

MIND: Microstructure INverse Design with Generative Hybrid Neural Representation

Tianyang Xue, Haochen Li, Longdu Liu +7

The inverse design of microstructures plays a pivotal role in optimizing metamaterials with specific, targeted physical properties. While traditional forward design methods are con…

cs.AI2024

GFPack++: Attention-Driven Gradient Fields for Optimizing 2D Irregular Packing

Tianyang Xue, Lin Lu, Yang Liu +5

2D irregular packing is a classic combinatorial optimization problem with various applications, such as material utilization and texture atlas generation. Due to its NP-hard nature…

cs.LG2023

Learning Gradient Fields for Scalable and Generalizable Irregular Packing

Tianyang Xue, Mingdong Wu, Lin Lu +3

The packing problem, also known as cutting or nesting, has diverse applications in logistics, manufacturing, layout design, and atlas generation. It involves arranging irregularly…