Analog circuit design requires substantial human expertise and involvement,
which is a significant roadblock to design productivity. Bayesian Optimization
(BO), a popular machine learning based optimization strategy, has been
leveraged to automate analog design given its applicability across various
circuit topologies and technologies. Traditional BO methods employ black box
Gaussian Process surrogate models and optimized labeled data queries to find
optimization solutions by trading off between exploration and exploitation.
However, the search for the optimal design solution in BO can be expensive from
both a computational and data usage point of view, particularly for high
dimensional optimization problems. This paper presents ADO-LLM, the first work
integrating large language models (LLMs) with Bayesian Optimization for analog
design optimization. ADO-LLM leverages the LLM's ability to infuse domain
knowledge to rapidly generate viable design points to remedy BO's inefficiency
in finding high value design areas specifically under the limited design space
coverage of the BO's probabilistic surrogate model. In the meantime, sampling
of design points evaluated in the iterative BO process provides quality
demonstrations for the LLM to generate high quality design points while
leveraging infused broad design knowledge. Furthermore, the diversity brought
by BO's exploration enriches the contextual understanding of the LLM and allows
it to more broadly search in the design space and prevent repetitive and
redundant suggestions. We evaluate the proposed framework on two different
types of analog circuits and demonstrate notable improvements in design
efficiency and effectiveness.

ADO-LLM 整合了大型语言模型与贝叶斯优化用于模拟设计优化，利用大型语言模型的领域知识并结合贝叶斯优化的多样性和探索能力实现了设计效率和效果的显著提升。

ADO-LLM: 基于上下文学习的模拟设计贝叶斯优化

ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of  Large Language Models

The complexity of BIM software presents significant barriers to the
widespread adoption of BIM and model-based design within the Architecture,
Engineering, and Construction (AEC) sector. End-users frequently express
concerns regarding the additional effort required to create a sufficiently
detailed BIM model when compared with conventional 2D drafting. This study
explores the potential of sequential recommendation systems to accelerate the
BIM modeling process. By treating BIM software commands as recommendable items,
we introduce a novel end-to-end approach that predicts the next-best command
based on user historical interactions. Our framework extensively preprocesses
real-world, large-scale BIM log data, utilizes the transformer architectures
from the latest large language models as the backbone network, and ultimately
results in a prototype that provides real-time command suggestions within the
BIM authoring tool Vectorworks. Subsequent experiments validated that our
proposed model outperforms the previous study, demonstrating the immense
potential of the recommendation system in enhancing design efficiency.

该研究探索了序贯推荐系统在加速 BIM 建模过程中的潜力，通过将 BIM 软件命令视为可推荐项目，引入了一种新颖的端到端方法，根据用户的历史交互预测下一个最佳命令，结果是在 BIM 创作工具 Vectorworks 中提供实时的命令建议。后续实验验证了我们提出的模型优于以往的研究，展示了推荐系统在提高设计效率方面的巨大潜力。

面向 BIM 创作工具的命令推荐系统的研究

Towards commands recommender system in BIM authoring tool using  transformers

Designing distributed filtering circuits (DFCs) is complex and
time-consuming, with the circuit performance relying heavily on the expertise
and experience of electronics engineers. However, manual design methods tend to
have exceedingly low-efficiency. This study proposes a novel end-to-end
automated method for fabricating circuits to improve the design of DFCs. The
proposed method harnesses reinforcement learning (RL) algorithms, eliminating
the dependence on the design experience of engineers. Thus, it significantly
reduces the subjectivity and constraints associated with circuit design. The
experimental findings demonstrate clear improvements in both design efficiency
and quality when comparing the proposed method with traditional engineer-driven
methods. In particular, the proposed method achieves superior performance when
designing complex or rapidly evolving DFCs. Furthermore, compared to existing
circuit automation design techniques, the proposed method demonstrates superior
design efficiency, highlighting the substantial potential of RL in circuit
design automation.

提出的自动化方法利用强化学习算法来改善分布式过滤电路（DFCs）的设计，消除了工程师设计经验的依赖，从而显著降低了电路设计的主观性和限制性。实验结果表明，与传统的工程师驱动方法相比，该方法在设计效率和质量上都有明显的改进，特别是在设计复杂或快速演化的 DFCs 时表现出卓越性能。此外，与现有的电路自动化设计技术相比，该方法展现出更高的设计效率，凸显了强化学习在电路设计自动化中的巨大潜力。