Harnessing Deep Learning to Solve Inverse Transient Heat Transfer With Periodic Boundary Condition
When information about an inaccessible domain of interest is needed, inverse problems
arise as measured data and available knowledge from a reachable domain are used to …
arise as measured data and available knowledge from a reachable domain are used to …
面内变刚度薄板弯曲问题的挠度− 弯矩耦合神经网络方法
黄钟民， 谢臻， 张易申， 彭林欣 - 力学学报, 2021 - lxxb.cstam.org.cn
发展了一种求解面内变刚度功能梯度薄板弯曲问题的神经网络方法. 面内变刚度薄板弯曲问题的
偏微分控制方程为一复杂的4 阶偏微分方程, 传统的基于强形式的神经网络解法在求解该偏微分 …
偏微分控制方程为一复杂的4 阶偏微分方程, 传统的基于强形式的神经网络解法在求解该偏微分 …
基于物理信息神经网络的多介质非线性瞬态热传导问题研究
陈豪龙， 唐欣越， 王润华， 周焕林， 柳占立 - 力学学报, 2024 - lxxb.cstam.org.cn
文章基于物理信息神经网络(physics-informed neural network, PINN) 求解多介质非线性瞬态
热传导问题. 根据多介质热物性参数的不同, 将求解区域划分成多个子域, 在每个子域中单独应用 …
热传导问题. 根据多介质热物性参数的不同, 将求解区域划分成多个子域, 在每个子域中单独应用 …
Deflection-bending moment coupling neural network method for the bending problem of thin plates with in-plane stiffness gradient
H Zhongmin, X Zhen, Z Yishen… - Chinese Journal of …, 2021 - lxxb.cstam.org.cn
A neural network method is developed to solve the bending problems of functionally graded
thin plates with in-plane stiffness gradient in this paper. The partial differential equation …
thin plates with in-plane stiffness gradient in this paper. The partial differential equation …
聚类分析-神经网络-贝叶斯优化联合识别复合材料参数研究
冯易鑫， 彭辉， 罗威 - 力学学报, 2024 - lxxb.cstam.org.cn
目前针对非均质复合材料参数的正逆向识别尚面临正向计算成本高和逆向识别泛用性低的难题.
数据驱动的计算均匀化方法可以一方面利用数据科学的先进算法降低控制方程的变量数目 …
数据驱动的计算均匀化方法可以一方面利用数据科学的先进算法降低控制方程的变量数目 …
An analytical solution to the one-dimensional unsteady temperature field near the Newtonian Cooling boundary
H Ren, Y Tao, T Wei, B Kang, Y Li, F Lin - Axioms, 2023 - mdpi.com
One-dimensional heat-conduction models in a semi-infinite domain, although forced
convection obeys Newton's law of cooling, are challenging to solve using standard integral …
convection obeys Newton's law of cooling, are challenging to solve using standard integral …
增材铜合金拉伸力学行为的卷积神经网络预测.
肖庆晖， 张仁嘉， 刘士杰， 胡文轩… - Chinese Journal of …, 2024 - search.ebscohost.com
摘要椇深度学习因其在处理复杂数据和解决复杂问题方面的显著优势而备受关注棳已应用于
材料性能预测领域暎本文提出了一种结合卷积神经网络模型与晶体塑性有限元方法的预测框架 …
材料性能预测领域暎本文提出了一种结合卷积神经网络模型与晶体塑性有限元方法的预测框架 …
[HTML][HTML] 基于弹性力学第一性原理的数据驱动力学建模
郑勇刚， 吴哲同， 张涵博， 刘振海， 叶宏飞… - 计算力学学报, 2024 - pubs.cstam.org.cn
提出了一种基于弹性力学第一性原理的数据驱动力学建模方法, 其能够从基于弹性力学方程的
数值计算结果建立简洁且能准确捕捉变形机制的力学模型. 基于有限元计算得到的高精度数据和 …
数值计算结果建立简洁且能准确捕捉变形机制的力学模型. 基于有限元计算得到的高精度数据和 …
The Laplace Transform Shortcut Solution to a One-Dimensional Heat Conduction Model with Dirichlet Boundary Conditions
D Wu, Y Tao, H Ren - Axioms, 2023 - mdpi.com
When using the Laplace transform to solve a one-dimensional heat conduction model with
Dirichlet boundary conditions, the integration and transformation processes become …
Dirichlet boundary conditions, the integration and transformation processes become …
Hybrid Quantum-Classical Machine Learning for Canonical Fluid Dynamics and Heat Transfer Problems
A Bazgir - 2024 - search.proquest.com
This thesis introduces the implementation of some hybrid frameworks for quantum and
classical machine learning techniques associated with the fluid dynamics and heat transfer …
classical machine learning techniques associated with the fluid dynamics and heat transfer …