![]() Sci China Phys Mech Astron 64(11):1–30ĭasari SK, Cheddad A, Andersson P (2019) Random forest surrogate models to support design space exploration in aerospace use-case. Struct Multidisc Optim 62(6):3127–3148Ĭhen X, Zhao X, Gong Z, Zhang J, Zhou W, Chen X, Yao W (2021) A deep neural network surrogate modeling benchmark for temperature field prediction of heat source layout. Clust Comput 17(2):327–337Ĭhen X, Chen X, Zhou W, Zhang J, Yao W (2020) The heat source layout optimization using deep learning surrogate modeling. Comput Methods Appl Mech Eng 345:363–381Ĭhen C, Taha TM (2014) A communication reduction approach to iteratively solve large sparse linear systems on a gpgpu cluster. J Heat Transf 143(6):060801Ĭapuano G, Rimoli JJ (2019) Smart finite elements: a novel machine learning application. In: 48th AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition, p 288Ĭai S, Wang Z, Wang S, Perdikaris P, Karniadakis GE (2021) Physics-informed neural networks for heat transfer problems. In: International conference on machine learning, PMLR, pp 2402–2411īodie M, Russell G, McCarthy K, Lucas E, Zumberge J, Wolff M (2010) Thermal analysis of an integrated aircraft model. Numerical results demonstrate that our method can significantly improve accuracy prediction on a smaller dataset while reducing the training time when compared with other CNN methods.īelbute-Peres FDA, Economon T, Kolter Z (2020) Combining differentiable pde solvers and graph neural networks for fluid flow prediction. Finally, combining the grid position information provided by the meshing surrogate with the scalar temperature field information provided by the thermal surrogate (combined model), we reach an end-to-end surrogate model from geometric parameters to temperature field prediction on an irregular geometric domain. Second, a physics-driven CNN surrogate with partial differential equation (PDE) residuals as a loss function is utilized for fast meshing (meshing surrogate) then, we present a data-driven surrogate model based on the multi-level reduced-order method, aiming to learn solutions of temperature field in the above regular computational plane (thermal surrogate). First, after adapting the Bezier curve in geometric parameterization, a body-fitted coordinate mapping is introduced to generate coordinate transforms between the irregular physical plane and regular computational plane. To alleviate this difficulty, we propose a novel physics and data co-driven surrogate modeling method. However, for temperature field prediction on irregular geometric domains (TFP-IGD), CNN can hardly be competent since most of them stem from processing for regular images. Recently, with the fast development of deep learning, several Convolutional Neural Network (CNN) surrogate models have been introduced to overcome this obstacle. But it faces a severe computational burden when directly applying traditional numerical analysis tools, especially when each optimization involves repetitive parameter modification and thermal analysis. ![]() In the whole aircraft structural optimization loop, thermal analysis plays a very important role. ![]()
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