Learning to simulate and design for structural engineering
- URL: http://arxiv.org/abs/2003.09103v3
- Date: Wed, 12 Aug 2020 23:21:21 GMT
- Title: Learning to simulate and design for structural engineering
- Authors: Kai-Hung Chang (1), Chin-Yi Cheng (1) ((1) Autodesk Research)
- Abstract summary: In this work, we formulate the building structures as graphs and create an end-to-end pipeline that can learn to propose the optimal cross-sections of columns and beams.
The performance of the proposed structural designs is comparable to the ones optimized by genetic algorithm (GA) with all the constraints satisfied.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The structural design process for buildings is time-consuming and laborious.
To automate this process, structural engineers combine optimization methods
with simulation tools to find an optimal design with minimal building mass
subject to building regulations. However, structural engineers in practice
often avoid optimization and compromise on a suboptimal design for the majority
of buildings, due to the large size of the design space, the iterative nature
of the optimization methods, and the slow simulation tools. In this work, we
formulate the building structures as graphs and create an end-to-end pipeline
that can learn to propose the optimal cross-sections of columns and beams by
training together with a pre-trained differentiable structural simulator. The
performance of the proposed structural designs is comparable to the ones
optimized by genetic algorithm (GA), with all the constraints satisfied. The
optimal structural design with the reduced the building mass can not only lower
the material cost, but also decrease the carbon footprint.
Related papers
- Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study [0.0]
We introduce a novel Physics-Informed DeepONet (PIDON) architecture to effectively model the nonlinear behavior of complex engineering systems.
We demonstrate the effectiveness of this framework in the optimization of aerospace-grade composites curing processes achieving a 3x speedup.
The proposed model has the potential to be used as a scalable and efficient optimization tool for broader applications in advanced engineering and digital twin systems.
arXiv Detail & Related papers (2025-02-17T07:11:46Z) - Cliqueformer: Model-Based Optimization with Structured Transformers [102.55764949282906]
Large neural networks excel at prediction tasks, but their application to design problems, such as protein engineering or materials discovery, requires solving offline model-based optimization (MBO) problems.
We present Cliqueformer, a transformer-based architecture that learns the black-box function's structure through functional graphical models (FGM)
Across various domains, including chemical and genetic design tasks, Cliqueformer demonstrates superior performance compared to existing methods.
arXiv Detail & Related papers (2024-10-17T00:35:47Z) - A novel design update framework for topology optimization with quantum annealing: Application to truss and continuum structures [0.0]
This paper presents a novel design update strategy for topology optimization, as an iterative optimization.
The key contribution lies in incorporating a design updater concept with quantum annealing, applicable to both truss and continuum structures.
Results indicate that the proposed framework successfully finds optimal topologies similar to benchmark results.
arXiv Detail & Related papers (2024-06-27T02:07:38Z) - Mechanistic Design and Scaling of Hybrid Architectures [114.3129802943915]
We identify and test new hybrid architectures constructed from a variety of computational primitives.
We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis.
We find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures.
arXiv Detail & Related papers (2024-03-26T16:33:12Z) - Compositional Generative Inverse Design [69.22782875567547]
Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem.
We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples.
In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
arXiv Detail & Related papers (2024-01-24T01:33:39Z) - Efficient Quality Diversity Optimization of 3D Buildings through 2D
Pre-optimization [101.18253437732933]
Quality diversity algorithms can be used to create a diverse set of solutions to inform engineers' intuition.
But quality diversity is not efficient in very expensive problems, needing 100.000s of evaluations.
We show that we can produce better machine learning models by producing training data with quality diversity.
arXiv Detail & Related papers (2023-03-28T11:20:59Z) - Investigating Positive and Negative Qualities of Human-in-the-Loop
Optimization for Designing Interaction Techniques [55.492211642128446]
Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives.
Model-based computational design algorithms assist designers by generating design examples during design.
Black box methods for assistance, on the other hand, can work with any design problem.
arXiv Detail & Related papers (2022-04-15T20:40:43Z) - Structural Design Recommendations in the Early Design Phase using
Machine Learning [6.071146161035648]
ApproxiFramer is a Machine Learning-based system for the automatic generation of structural layouts from building plan sketches in real-time.
We trained a Convolutional Neural Net to iteratively generate structural design solutions for sketch-level building plans.
arXiv Detail & Related papers (2021-07-19T01:02:14Z) - Biogeography-Based Optimization of RC structures including static
soil-structure interaction [0.0]
We present a method to minimize the cost of the structural design of reinforced concrete structures using Biogeography-Based Optimization.
SAP2000 is used as computational engine, taking into account modelling aspects such as static soil-structure interaction (SSSI)
arXiv Detail & Related papers (2021-03-08T22:48:04Z) - An AI-Assisted Design Method for Topology Optimization Without
Pre-Optimized Training Data [68.8204255655161]
An AI-assisted design method based on topology optimization is presented, which is able to obtain optimized designs in a direct way.
Designs are provided by an artificial neural network, the predictor, on the basis of boundary conditions and degree of filling as input data.
arXiv Detail & Related papers (2020-12-11T14:33:27Z) - Topology optimization of 2D structures with nonlinearities using deep
learning [0.0]
Cloud computing has made it possible to search for optimal nonlinear structures.
We develop convolutional neural network models to predict optimized designs.
The developed models are capable of accurately predicting the optimized designs without requiring an iterative scheme.
arXiv Detail & Related papers (2020-01-31T12:36:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.