Engineering design optimization requires an efficient combination of a 3D
shape representation, an optimization algorithm, and a design performance
evaluation method, which is often computationally expensive. We present a
prompt evolution design optimization (PEDO) framework contextualized in a
vehicle design scenario that leverages a vision-language model for penalizing
impractical car designs synthesized by a generative model. The backbone of our
framework is an evolutionary strategy coupled with an optimization objective
function that comprises a physics-based solver and a vision-language model for
practical or functional guidance in the generated car designs. In the prompt
evolutionary search, the optimizer iteratively generates a population of text
prompts, which embed user specifications on the aerodynamic performance and
visual preferences of the 3D car designs. Then, in addition to the
computational fluid dynamics simulations, the pre-trained vision-language model
is used to penalize impractical designs and, thus, foster the evolutionary
algorithm to seek more viable designs. Our investigations on a car design
optimization problem show a wide spread of potential car designs generated at
the early phase of the search, which indicates a good diversity of designs in
the initial populations, and an increase of over 20\% in the probability of
generating practical designs compared to a baseline framework without using a
vision-language model. Visual inspection of the designs against the performance
results demonstrates prompt evolution as a very promising paradigm for finding
novel designs with good optimization performance while providing ease of use in
specifying design specifications and preferences via a natural language
interface.

工程设计优化需要高效结合三维形状表示、优化算法和设计性能评估方法，我们提出了一种在车辆设计场景中利用视觉语言模型来惩罚生成模型合成的不切实际汽车设计的快速演化设计优化 (PEDO) 框架。

基于生成式 AI 的提示演化工程设计优化与视觉语言模型

Generative AI-based Prompt Evolution Engineering Design Optimization  With Vision-Language Model

For partial differential equations on domains of arbitrary shapes, existing
works of neural operators attempt to learn a mapping from geometries to
solutions. It often requires a large dataset of geometry-solution pairs in
order to obtain a sufficiently accurate neural operator. However, for many
industrial applications, e.g., engineering design optimization, it can be
prohibitive to satisfy the requirement since even a single simulation may take
hours or days of computation. To address this issue, we propose reference
neural operators (RNO), a novel way of implementing neural operators, i.e., to
learn the smooth dependence of solutions on geometric deformations.
Specifically, given a reference solution, RNO can predict solutions
corresponding to arbitrary deformations of the referred geometry. This approach
turns out to be much more data efficient. Through extensive experiments, we
show that RNO can learn the dependence across various types and different
numbers of geometry objects with relatively small datasets. RNO outperforms
baseline models in accuracy by a large lead and achieves up to 80% error
reduction.

提出了一种新颖的神经算子实现方式 —— 参考神经算子，通过学习解对几何形变的平滑依赖性，可以在相对较小的数据集上学习不同类型和数量几何对象的依赖关系，并在准确性上优于基线模型达到高达 80％的错误减少。

参考神经算子：学习偏微分方程解对几何变形的平滑依赖关系

Reference Neural Operators: Learning the Smooth Dependence of Solutions  of PDEs on Geometric Deformations

Designing an inexpensive approximate surrogate model that captures the
salient features of an expensive high-fidelity behavior is a prevalent approach
in design optimization. In recent times, Deep Learning (DL) models are being
used as a promising surrogate computational model for engineering problems.
However, the main challenge in creating a DL-based surrogate is to
simulate/label a large number of design points, which is time-consuming for
computationally costly and/or high-dimensional engineering problems. In the
present work, we propose a novel sampling technique by combining the active
learning (AL) method with DL. We call this method $\epsilon$-weighted hybrid
query strategy ($\epsilon$-HQS) , which focuses on the evaluation of the
surrogate at each learning iteration and provides an estimate of the failure
probability of the surrogate in the Design Space. By reusing already collected
training and test data, the learned failure probability guides the next
iteration's sampling process to the region of the high probability of failure.
During the empirical evaluation, better accuracy of the surrogate was observed
in comparison to other methods of sample selection. We empirically evaluated
this method in two different engineering design domains, finite element based
static stress analysis of submarine pressure vessel(computationally costly
process) and second submarine propeller design( high dimensional problem).
this https URL

该研究提出一种新的采样技术，将主动学习方法与深度学习相结合，实现基于代理的建模在工程设计优化中的应用，并经过两个不同工程设计领域的实证评估，结果显示该新方法在样本选择方面比其他方法更准确。