Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites
We present a stochastic integrated machine learning based multiscale approach for the
prediction of the macroscopic thermal conductivity in carbon nanotube reinforced polymeric …
prediction of the macroscopic thermal conductivity in carbon nanotube reinforced polymeric …
Ensemble machine-learning models for accurate prediction of solar irradiation in Bangladesh
Improved irradiance forecasting ensures precise solar power generation forecasts, resulting
in smoother operation of the distribution grid. Empirical models are used to estimate …
in smoother operation of the distribution grid. Empirical models are used to estimate …
[HTML][HTML] Ai for pdes in solid mechanics: A review
In recent years, deep learning has become ubiquitous and is empowering various fields. In
particular, the combination of artificial intelligence and traditional science (AI for science …
particular, the combination of artificial intelligence and traditional science (AI for science …
Comparative analysis of twelve transfer learning models for the prediction and crack detection in concrete dams, based on borehole images
US Khan, M Ishfaque, SUR Khan, F Xu, L Chen… - Frontiers of Structural …, 2024 - Springer
Disaster-resilient dams require accurate crack detection, but machine learning methods
cannot capture dam structural reaction temporal patterns and dependencies. This research …
cannot capture dam structural reaction temporal patterns and dependencies. This research …
Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design
We propose a computational framework using surrogate models through five steps, which
can systematically and comprehensively address a number of related stochastic multi-scale …
can systematically and comprehensively address a number of related stochastic multi-scale …
Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites
We introduce an interpretable stochastic integrated machine learning based multiscale
approach for the prediction of the macroscopic thermal conductivity in Polymeric graphene …
approach for the prediction of the macroscopic thermal conductivity in Polymeric graphene …
Proposed numerical and machine learning models for fiber-reinforced polymer concrete-steel hollow and solid elliptical columns
This study employs a hybrid approach, integrating finite element method (FEM) simulations
with machine learning (ML) techniques to investigate the structural performance of double …
with machine learning (ML) techniques to investigate the structural performance of double …
Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and …
In this work, we present a deep collocation method (DCM) for three-dimensional potential
problems in non-homogeneous media. This approach utilizes a physics-informed neural …
problems in non-homogeneous media. This approach utilizes a physics-informed neural …
Physics-informed deep learning for melting heat transfer analysis with model-based transfer learning
We present an adaptive deep collocation method (DCM) based on physics-informed deep
learning for the melting heat transfer analysis of a non-Newtonian (Sisko) fluid over a …
learning for the melting heat transfer analysis of a non-Newtonian (Sisko) fluid over a …
Pre-training strategy for solving evolution equations based on physics-informed neural networks
J Guo, Y Yao, H Wang, T Gu - Journal of Computational Physics, 2023 - Elsevier
The physics informed neural network (PINN) is a promising method for solving time-
evolution partial differential equations (PDEs). However, the standard PINN method may fail …
evolution partial differential equations (PDEs). However, the standard PINN method may fail …