Deep Generative Models in Engineering Design: A Review

In plain words

Engineers design products such as airfoils, bicycles, trusses and metamaterials. Deep generative models are neural networks that learn from many existing designs and then produce new ones. This review explains the main model families used for that. GANs pit a generator against a discriminator. VAEs learn a compact space of designs that can be sampled. Reinforcement learning builds a design step by step and rewards it for how well it performs.

The hard part is that a generated design has to work physically, be buildable, and sometimes be new, and copying the training data does not achieve that. The review also shows the ways a design can be stored on a computer, such as pixels, 3D voxels, point clouds, meshes or lists of parameters, and each choice has trade-offs. It then catalogues the applications and public datasets found in the literature and points to the weak spots: checking performance cheaply and accurately, respecting constraints, creativity, and scarce data.

TL;DR

• A survey, with no experiments of its own, of deep generative models in engineering design: GANs, VAEs and reinforcement learning §1 §2.
• It reviews 63 papers from 2014 to September 2021, classified by domain and architecture §4 tab. 1.
• It compares design representations (images, voxels, point clouds, meshes, signed distance fields, parameters, grammars, graphs) with their pros and cons §3.
• It lists public datasets, from topology optimization to CAD, metamaterials, sketches and human design sequences §6.
• It names the open challenges: performance evaluation, constraints and manufacturability, creativity and novelty, and data sparsity with no shared benchmarks §7.

Use it as a map of the field and its datasets. It does not give an implementable method.

Problem

Engineering design needs outputs that meet performance targets, can be fabricated and, where the goal is creativity, differ from what already exists. The review lists four obstacles: modelling performance (inverse design is costly to evaluate), sparse data, the creativity gap (generative models are trained to imitate existing designs), and usability and feasibility (the design must be convertible into a fabricable form) §1.

Scope: the review covers deep learning works that use GANs, VAEs or RL for engineering design. Works that only do design optimisation are excluded. Other domains such as computer science are out of scope for the selected papers, but background works on fundamentals, applications and datasets need not meet these rules §4.

Method

This is a review, so its "method" is a taxonomy. Section numbers refer to the compiled paper (v4 LaTeX source).

Model families §2

1. GANs §2.1. A generator maps noise to designs. A discriminator learns to tell real from generated samples, and the two are trained against each other. Typical failure modes are vanishing generator gradients when the discriminator wins, and mode collapse. Remedies include the Wasserstein critic (WGAN) and diversity rewards. Conditioning comes in three forms: a cGAN feeds a condition vector to both networks; InfoGAN has an auxiliary network reconstruct the condition; and continuous-condition variants include Regressional GAN, CcGAN and PcDGAN §2.1.
2. VAEs §2.2. An encoder maps a design to a latent distribution (a mean and a variance per latent dimension), a latent vector is sampled, and a decoder reconstructs the design. A Kullback–Leibler term pulls the latent distribution towards a standard Gaussian. A conditional VAE adds the condition vector to both encoder and decoder §2.2.
3. Reinforcement learning §2.3. An actor takes actions in an environment and maximises reward, so no dataset is needed. In design, the process is usually a sequence of building or altering a design, and the reward is the quality of the result. Rewards need not be differentiable, unlike the losses of GANs and VAEs, but they require reliable signals, often from high-fidelity simulation §2.3.

Design representations §3 (each with pros and cons in the review)

• Images: information-rich and convolution-friendly, but pixel outputs are often infeasible for fabrication or simulation without conversion §3.1.
• Voxels: support 3D convolution, but parameter counts grow with the cube of the resolution §3.2.
• Point clouds: arbitrary geometry from few points, often converted to meshes or BRep before use §3.3.
• Meshes: directly simulatable, but harder for ML to generate §3.4.
• Signed distance fields: a convenient learning intermediate, still needing conversion §3.5.
• Parameterisations: information-dense and potentially fabricable, but mixed data types, skewed distributions and sparse data make learning hard §3.6.
• Grammars: enforce feasibility by construction, but must usually be written by hand and can limit exploration §3.7.
• Graphs: flexible for systems and assemblies, with few design graph datasets §3.8.

Literature search §4

• Search terms "Generative Adversarial Network", "Variational Autoencoder" and "Reinforcement Learning" in Google Scholar, first limited to five design venues (JMD, IDETC, CAD, ICED, AIEDAM) to seed the set §4.
• Two raters, who are authors, decided relevance independently. Disagreements were resolved by discussion among all authors §4.
• Papers cited by the seed papers were added, giving 41 seed papers and 22 from other venues, 63 in total §4.

Application domains §5 (classified in tab. 1)

• Topology optimisation §5.1: networks predict optimised topologies directly or initialise a conventional solver. Iterative methods retrain on newly optimised designs and drop near-duplicates. Super-resolution models upscale low-resolution topologies, and physics-aware losses improve topology GANs.
• Microstructures and metamaterials §5.2: reconstruction with GANs, VAEs or CDBNs, often with style-transfer losses. Inverse design maps latent variables to properties with a regressor and then optimises them with a genetic algorithm or Bayesian optimisation. Unit-cell VAEs are a further branch §5.2.
• 2D shape synthesis §5.3: airfoils dominate, with performance-conditioned cGANs, Bézier-curve GANs, and PaDGAN, which uses a determinantal point process for diversity §5.3.
• 3D shape synthesis §5.4: mostly performance-aware, with GA optimisation of VAE latents, iterative GAN retraining on CFD-scored aircraft, and range loss with self-augmentation §5.4.
• Other §5.5: manufacturability, flow-channel RL, human-in-the-loop design, kinematic synthesis, truss design, hierarchical products (sailing craft, bicycles) and procedural VR content §5.5.

Iterative pattern used in several surveyed works (our summary of §5.1 and §7.1; the review gives no code):

# Iterative retraining with performance checks  (summary, not an algorithm in the paper)
# Input:  designs (training set), generator, evaluate (simulator or surrogate)
# Output: generator trained on a revised design set

def iterative_retraining(designs, generator, evaluate, num_steps):
    # 1. Fit the generative model to the current designs  [§5.1] [§7.1]
    generator = train(generator, designs)
    for step in range(num_steps):
        # 2. Sample candidates and score them  [§7.1]
        candidates = sample(generator)
        scores = evaluate(candidates)
        # 3. Keep high-scoring or novel designs and refit  [§5.1] [§7.1]
        designs = select(designs, candidates, scores)
        generator = train(generator, designs)
    return generator

The review notes that retraining is typically done only a handful of times with medium-fidelity evaluation, and that such methods do not use gradients of the performance score §7.1.

Hyperparameters

Not applicable: the review runs no experiments and states no training hyperparameters for any surveyed method. The settings it does state concern the literature search.

Results

Author-reported, with no experiments. The review gives the literature counts and the size of each dataset it describes. These numbers come from the review and were not checked against the original dataset papers.

Reported results from the surveyed works, for example near-optimal topologies from an upscaling pipeline "many times faster" than classic TO §5.1, or 98% binary accuracy after five TO iterations versus 100 for the full run §5.1, are quoted as the review states them and are not verified here.

Paper vs code

No official code. The review does not link code of its own. The abstract and the LaTeX source contain only dataset and index links (for example the dataset footnotes in §6.1 and §6.2). Under the survey rule, a linked paper index or dataset is not code, so no code field is recorded and no repository was cloned.

Pitfalls

• Date window is inconsistent. The scope says Jan. 2014 to Sep. 2021 §4, but the reported counts only cover 2016 onwards: 15 papers from 2016 to 2018 and 48 from 2019 to Aug. 2021 §4. The abstract says the field has grown "since 2016" [abstract]. Do not report a precise growth curve from this review: the publication-year figure is commented out in the source §4.
• No equations or algorithms are numbered in the compiled text. The equation blocks for RL returns and the Q-function are commented out §2.3. Cite the sections, not equation numbers.
• Mnih et al. date. The text says the first deep Q-learning work was in 2013, but cites a 2015 paper §2.3. The review does not resolve the discrepancy, so cite the 2015 reference if the year matters.
• Figure label mismatch. The 3D shape figure has the label fig:Unitcells, but its caption concerns Range-GAN aircraft models fig. 5. Cite it by caption and number, not by label.
• Selection bias. The set is built from three search terms and a few venues, then from citations of those papers §4. The application table is the authors' classification, and some papers fit several rows tab. 1. Coverage is not claimed to be complete.
• Rater agreement not stated. The paper says two raters decided relevance, but gives no agreement measure §4.
• Dataset numbers are the review's own. The sizes in the Results table were copied from the review's text and may differ from the original dataset papers.
• Conversion costs hidden in representations. Images, voxels, point clouds, meshes and SDFs usually need conversion to BRep or polygon form before simulation or fabrication §3.1 §3.2 §3.3 §3.5. Parametric data avoids this but is harder to learn §3.6.
• Surrogate models are the usual shortcut. Most surveyed works use low-cost surrogates for evaluation, and the review warns they generalise poorly outside the training data §7.1.
• Constraint handling is weak. The review notes that DGMs rarely satisfy explicit constraints reliably, and that manufacturability is seldom modelled §7.2.
• Creativity is rarely modelled. Most DGMs imitate their training data, and the review identifies few methods that model creativity or novelty §7.3.
• Transformers and diffusion models. The review mentions transformer-based sequence models as a possible direction, but says they are very large and need large datasets §7.6. Diffusion models appear only in commented-out text in the source, so the review does not assess them.

Lineage

• Builds on: grammar-based design synthesis survey by Chakrabarti et al. §3.7; microstructure characterisation and reconstruction review by Bostanabad et al. §5.2; creativity survey by Franceschelli et al. §7.3.
• Compared against: not applicable, the review runs no benchmark.
• Superseded by: not known from content already in this wiki.