In this paper, we introduce LInK, a novel framework that integrates
contrastive learning of performance and design space with optimization
techniques for solving complex inverse problems in engineering design with
discrete and continuous variables. We focus on the path synthesis problem for
planar linkage mechanisms. By leveraging a multi-modal and
transformation-invariant contrastive learning framework, LInK learns a joint
representation that captures complex physics and design representations of
mechanisms, enabling rapid retrieval from a vast dataset of over 10 million
mechanisms. This approach improves precision through the warm start of a
hierarchical unconstrained nonlinear optimization algorithm, combining the
robustness of traditional optimization with the speed and adaptability of
modern deep learning methods. Our results on an existing benchmark demonstrate
that LInK outperforms existing methods with 28 times less error compared to a
state-of-the-art approach while taking 20 times less time on an existing
benchmark. Moreover, we introduce a significantly more challenging benchmark,
named LINK-ABC, which involves synthesizing linkages that trace the
trajectories of English capital alphabets - an inverse design benchmark task
that existing methods struggle with due to large non-linearities and tiny
feasible space. Our results demonstrate that LInK not only advances the field
of mechanism design but also broadens the applicability of contrastive learning
and optimization to other areas of engineering.

该论文引入了 LInK，这是一个将性能和设计空间的对比学习与工程设计中的复杂反问题的优化技术相结合的新型框架，针对平面连杆机构的路径合成问题，通过多模态和变换不变性对比学习框架的应用，LInK 学习到了一种捕捉机构复杂物理学和设计表征的联合表示，可以从超过 1000 万个机构的庞大数据集中快速检索，该方法通过基于层次的无约束非线性优化算法的热启动，将传统优化的鲁棒性与现代深度学习方法的速度和适应性相结合，实验结果表明，与最先进的方法相比，LInK 在现有基准测试上的误差减少了 28 倍，而计算时间减少了 20 倍，此外，该论文还提出了一个更具挑战性的基准测试 LINK-ABC，该测试涉及综合链接以追踪英文大写字母的轨迹 - 由于非线性关联和微小的可行空间，现有方法在此任务上面临困难，通过研究结果表明，LInK 不仅推动了机构设计领域的发展，而且拓宽了对比学习和优化在其他工程领域的适用性。

通过对机构合成的对比学习，学习设计和性能空间的联合表示

LInK: Learning Joint Representations of Design and Performance Spaces  through Contrastive Learning for Mechanism Synthesis

Extending the success of 2D Large Kernel to 3D perception is challenging due
to: 1. the cubically-increasing overhead in processing 3D data; 2. the
optimization difficulties from data scarcity and sparsity. Previous work has
taken the first step to scale up the kernel size from 3x3x3 to 7x7x7 by
introducing block-shared weights. However, to reduce the feature variations
within a block, it only employs modest block size and fails to achieve larger
kernels like the 21x21x21. To address this issue, we propose a new method,
called LinK, to achieve a wider-range perception receptive field in a
convolution-like manner with two core designs. The first is to replace the
static kernel matrix with a linear kernel generator, which adaptively provides
weights only for non-empty voxels. The second is to reuse the pre-computed
aggregation results in the overlapped blocks to reduce computation complexity.
The proposed method successfully enables each voxel to perceive context within
a range of 21x21x21. Extensive experiments on two basic perception tasks, 3D
object detection and 3D semantic segmentation, demonstrate the effectiveness of
our method. Notably, we rank 1st on the public leaderboard of the 3D detection
benchmark of nuScenes (LiDAR track), by simply incorporating a LinK-based
backbone into the basic detector, CenterPoint. We also boost the strong
segmentation baseline's mIoU with 2.7% in the SemanticKITTI test set. Code is
available at this https URL.

提出了一种名为 LinK 的方法，通过使用线性卷积核生成器和重复利用重叠块中预先计算的聚合结果来扩展感受野，从而成功实现每个体素在 21x21x21 范围内感知上下文，并在三维物体检测和语义分割任务中取得了显着表现。