Diffusion Models Beat GANs on Topology Optimization

Mazé, François; Ahmed, Faez

doi:10.1609/aaai.v37i8.26093

Cited by 15 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These challenges include the limited availability of publicly accessible data (1), the minimal margin for error in predictions (2), the fact that most data usually is somehow related to 3D models, and the inherent complexity in designing and manufacturing components and assemblies (3). Furthermore, there exists a significant knowledge gap between data scientists and mechanical engineers (4). Typically, one group lacks the necessary foundational understanding of the other field.…”

Section: Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning In Design Engineering and Manufacturing

Villena Toro

2023

Linköping Studies in Science and Technology. Licentiate Thesis

View full text Add to dashboard Cite

Artificial intelligence (AI) has made significant strides in various fields, challenging conventional notions of computer capabilities. However, while data science research primarily concentrates on refining AI models, there are numerous challenges associated with integrating AI into industrial applications.Knowledge-Based Engineering, with its potential to streamline the production cycle by reusing engineering knowledge and intent, emerges as a promising avenue for AI in the industry. When engineering knowledge is effectively processed and categorized, neural networks naturally emerge as potent tools for automation. This thesis presents three case studies that demonstrate the practicality of supervised learning, particularly in the domain of neural networks, to address manufacturing automation challenges. These case studies span various stages of the manufacturing system, encompassing engineering design, production planning, and quality control phases. The first application employs supervised learning to automate the generation of engineering drawings, while the third employs optical character recognition to expedite the quality control process for complex engineering drawings. The second application centers on the estimation of fixturing clamps for welding operations in automobile parts.In summary, this thesis strives to make a meaningful contribution to the field of design engineering and manufacturing by examining the potential of AI in enhancing processes and addressing automation hurdles. By presenting case studies that showcase the utility of machine learning models in production settings, this thesis aims to stimulate further research in this evolving field.iii Realization, Linköping University. I would like to express my gratitude to everyone involved in the research projects and the rest of the division. Special thanks go to my supervisory team: Mehdi Tarkian, for believing in me and providing constant motivation; Anton Wiberg, for valuable insights and our extensive discussions about possible future projects; Peter Hallberg and Johan Ölvander, for their senior assessment and guidance. I want to thank both current and former Ph.D. students for their inspiration and for creating such a great working environment, especially my office roommates and the Spanish speakers on the other side of the corridor.I also extend my thanks to my parents, sister, and grandparents for their constant support from abroad, as well as to Irene, my life partner, for her support on a daily basis. Finally, thanks to you, for opening this thesis and reading it, at least until the end of the acknowledgments. v Appended PublicationsPapers I-IV presented below comprise the foundation of this thesis. The papers are referred to with bold capital romans.

show abstract

Section: Motivationmentioning

confidence: 99%

“…Design engineering datasets typically consist of thousands of samples, a far cry from the trillions found in text or image datasets. To overcome the scarcity of data, engineers can generate synthetic data, for instance, through parametric models [3,4].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning In Design Engineering and Manufacturing

Villena Toro

2023

Linköping Studies in Science and Technology. Licentiate Thesis

View full text Add to dashboard Cite

show abstract

“…In addition to the 30,000 feasible hulls in the Ship-D dataset, an additional 20,000 design vectors (called invalid samples) that violate at least one feasibility constraint were generated. These invalid samples were used to train models in classifying and distinguishing between feasible and infeasible design vectors [41].…”

Section: Feasibility Constraints For Hull Geometrymentioning

confidence: 99%

“…Guided diffusion can be applied to engineering design generation. For example, guided diffusion has been used to create two-dimensional structures [41][42][43] and vehicles [44] using image data. In these instances, the guidance of the design generation using image-based DDPMs is applied for constraint satisfaction and improved performance.…”

mentioning

confidence: 99%

ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints

Bagazinski,

Ahmed

2023

JMSE

Self Cite

View full text Add to dashboard Cite

Ship design is a years-long process that requires balancing complex design trade-offs to create a ship that is efficient and effective. Finding new ways to improve the ship design process could lead to significant cost savings in the time and effort required to design a ship, as well as cost savings in the procurement and operation of a ship. One promising technology is generative artificial intelligence, which has been shown to reduce design cycle times and create novel, high-performing designs. In a literature review, generative artificial intelligence was shown to generate ship hulls; however, ship design is particularly difficult, as the hull of a ship requires the consideration of many objectives. This paper presents a study on the generation of parametric ship hull designs using a parametric diffusion model that considers multiple objectives and constraints for hulls. This denoising diffusion probabilistic model (DDPM) generates the tabular parametric design vectors of a ship hull, which are then constructed into a point cloud and mesh for performance evaluation. In addition to a tabular DDPM, this paper details adding guidance to improve the quality of the generated parametric ship hull designs. By leveraging a classifier to guide sample generation, the DDPM produced feasible parametric ship hulls that maintained the coverage of the initial training dataset of ship hulls with a 99.5% rate, a 149× improvement over random sampling of the design vector parameters across the design space. Parametric ship hulls produced using performance guidance saw an average 91.4% reduction in wave drag coefficients and an average 47.9× relative increase in the total displaced volume of the hulls compared to the mean performance of the hulls in the training dataset. The use of a DDPM to generate parametric ship hulls can reduce design times by generating high-performing hull designs for future analysis. These generated hulls have low drag and high volume, which can reduce the cost of operating a ship and increase its potential to generate revenue.

show abstract

DMF-TONN: Direct Mesh-free Topology Optimization using Neural Networks

Joglekar,

Chen,

Kara

2023

Engineering with Computers

View full text Add to dashboard Cite

We propose a direct mesh-free method for performing topology optimization by integrating a density field approximation neural network with a displacement field approximation neural network. We show that this direct integration approach can give comparable results to conventional topology optimization techniques, with an added advantage of enabling seamless integration with post-processing software, and a potential of topology optimization with objectives where meshing and Finite Element Analysis (FEA) may be expensive or not suitable. Our approach (DMF-TONN) takes in as inputs the boundary conditions and domain coordinates and finds the optimum density field for minimizing the loss function of compliance and volume fraction constraint violation. The mesh-free nature is enabled by a physics-informed displacement field approximation neural network to solve the linear elasticity partial differential equation and replace the FEA conventionally used for calculating the compliance. We show that using a suitable Fourier Features neural network architecture and hyperparameters, the density field approximation neural network can learn the weights to represent the optimal density field for the given domain and boundary conditions, by directly backpropagating the loss gradient through the displacement field approximation neural network, and unlike prior work there is no requirement of a sensitivity filter, optimality criterion method, or a separate training of density network in each topology optimization iteration.

show abstract

Diffusion Models Beat GANs on Topology Optimization

Cited by 15 publications

References 24 publications

Machine Learning In Design Engineering and Manufacturing

Machine Learning In Design Engineering and Manufacturing

ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints

DMF-TONN: Direct Mesh-free Topology Optimization using Neural Networks

Contact Info

Product

Resources

About