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Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks

arXiv 2201.10002 ↗First posted 21 Jan 2022cs.LG cs.CEcited by 0

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Abstract (arXiv)

The layout optimization of the heat conduction is essential during design in engineering, especially for thermal sensible products. When the optimization algorithm iteratively evaluates different loading cases, the traditional numerical simulation methods used usually lead to a substantial computational cost. To effectively reduce the computational effort, data-driven approaches are used to train a surrogate model as a mapping between the prescribed external loads and various geometry. However, the existing model are trained by data-driven methods which requires intensive training samples that from numerical simulations and not really effectively solve the problem. Choosing the steady heat conduction problems as examples, this paper proposes a Physics-driven Convolutional Neural Networks (PD-CNN) method to infer the physical field solutions for random varied loading cases. After that, the Particle Swarm Optimization (PSO) algorithm is used to optimize the sizes and the positions of the hole masks in the prescribed design domain, and the average temperature value of the entire heat conduction field is minimized, and the goal of minimizing heat transfer is achieved. Compared with the existing data-driven approaches, the proposed PD-CNN optimization framework not only predict field solutions that are highly consistent with conventional simulation results, but also generate the solution space with without any pre-obtained training data.

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References · 18

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2016An overview of gradient descent optimization algorithmscited by 1,021
  5. 2019DeepXDE: A deep learning library for solving differential equationscited by 617
  6. 2019Machine Learning for Fluid Mechanicscited by 380
  7. 2017Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equationscited by 362
  8. 2019Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Datacited by 261
  9. 2018Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flowscited by 147
  10. 2019Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networkscited by 144
  11. 2019Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networkscited by 127
  12. 2017Neural networks for topology optimizationcited by 39
6 more
  1. 2017Deep Learning the Physics of Transport Phenomenacited by 26
  2. 2019U-net architectures for fast prediction of incompressible laminar flowscited by 16
  3. 2018Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Losscited by 15
  4. 2019Surrogate Optimization of Deep Neural Networks for Groundwater Predictionscited by 6
  5. 2017Shape optimization in laminar flow with a label-guided variational autoencodercited by 5
  6. 2020A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolutional Neural Networkscited by 5

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