TY - JOUR A1 - Budarapu, Pattabhi Ramaiah A1 - Gracie, Robert A1 - Yang, Shih-Wei A1 - Zhuang, Xiaoying A1 - Rabczuk, Timon T1 - Efficient Coarse Graining in Multiscale Modeling of Fracture JF - Theoretical and Applied Fracture Mechanics N2 - Efficient Coarse Graining in Multiscale Modeling of Fracture KW - Angewandte Mathematik KW - Strukturmechanik Y1 - 2014 SP - 126 EP - 143 ER - TY - JOUR A1 - Chakraborty, Ayan A1 - Anitescu, Cosmin A1 - Zhuang, Xiaoying A1 - Rabczuk, Timon T1 - Domain adaptation based transfer learning approach for solving PDEs on complex geometries JF - Engineering with Computers N2 - In machine learning, if the training data is independently and identically distributed as the test data then a trained model can make an accurate predictions for new samples of data. Conventional machine learning has a strong dependence on massive amounts of training data which are domain specific to understand their latent patterns. In contrast, Domain adaptation and Transfer learning methods are sub-fields within machine learning that are concerned with solving the inescapable problem of insufficient training data by relaxing the domain dependence hypothesis. In this contribution, this issue has been addressed and by making a novel combination of both the methods we develop a computationally efficient and practical algorithm to solve boundary value problems based on nonlinear partial differential equations. We adopt a meshfree analysis framework to integrate the prevailing geometric modelling techniques based on NURBS and present an enhanced deep collocation approach that also plays an important role in the accuracy of solutions. We start with a brief introduction on how these methods expand upon this framework. We observe an excellent agreement between these methods and have shown that how fine-tuning a pre-trained network to a specialized domain may lead to an outstanding performance compare to the existing ones. As proof of concept, we illustrate the performance of our proposed model on several benchmark problems. KW - Maschinelles Lernen KW - NURBS KW - Transfer learning KW - Domain Adaptation KW - NURBS geometry KW - Navier–Stokes equations Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220811-46776 UR - https://link.springer.com/article/10.1007/s00366-022-01661-2 VL - 2022 SP - 1 EP - 20 ER - TY - JOUR A1 - Ghasemi, Hamid A1 - Brighenti, Roberto A1 - Zhuang, Xiaoying A1 - Muthu, Jacob A1 - Rabczuk, Timon T1 - Optimum fiber content and distribution in fiber-reinforced solids using a reliability and NURBS based sequential optimization approach JF - Structural and Multidisciplinary Optimization N2 - Optimum _ber content and distribution in _ber-reinforced solids using a reliability and NURBS based sequential optimization approach KW - Angewandte Mathematik KW - Strukturmechanik Y1 - 2015 SP - 99 EP - 112 ER - TY - JOUR A1 - Ghasemi, Hamid A1 - Brighenti, Roberto A1 - Zhuang, Xiaoying A1 - Muthu, Jacob A1 - Rabczuk, Timon T1 - Optimization of fiber distribution in fiber reinforced composite by using NURBS functions JF - Computational Materials Science N2 - Optimization of fiber distribution in fiber reinforced composite by using NURBS functions KW - Angewandte Mathematik KW - Strukturmechanik Y1 - 2014 SP - 463 EP - 473 ER - TY - JOUR A1 - Ghasemi, Hamid A1 - Brighenti, Roberto A1 - Zhuang, Xiaoying A1 - Muthu, Jacob A1 - Rabczuk, Timon T1 - Sequential reliability based optimization of fiber content and dispersion in fiber reinforced composite by using NURBS finite elements JF - Structural and Multidisciplinary Optimization N2 - Sequential reliability based optimization of fiber content and dispersion in fiber reinforced composite by using NURBS finite elements KW - Angewandte Mathematik KW - Strukturmechanik Y1 - 2014 ER - TY - JOUR A1 - Ghasemi, Hamid A1 - Rafiee, Roham A1 - Zhuang, Xiaoying A1 - Muthu, Jacob A1 - Rabczuk, Timon T1 - Uncertainties propagation in metamodel-based probabilistic optimization of CNT/polymer composite structure using stochastic multi-scale modeling JF - Computational Materials Science N2 - Uncertainties propagation in metamodel-based probabilistic optimization of CNT/polymer composite structure using stochastic multi-scale modeling KW - Angewandte Mathematik KW - Strukturmechanik Y1 - 2014 SP - 295 EP - 305 ER - TY - JOUR A1 - Guo, Hongwei A1 - Alajlan, Naif A1 - Zhuang, Xiaoying A1 - Rabczuk, Timon T1 - Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials JF - Computational Mechanics N2 - We present a physics-informed deep learning model for the transient heat transfer analysis of three-dimensional functionally graded materials (FGMs) employing a Runge–Kutta discrete time scheme. Firstly, the governing equation, associated boundary conditions and the initial condition for transient heat transfer analysis of FGMs with exponential material variations are presented. Then, the deep collocation method with the Runge–Kutta integration scheme for transient analysis is introduced. The prior physics that helps to generalize the physics-informed deep learning model is introduced by constraining the temperature variable with discrete time schemes and initial/boundary conditions. Further the fitted activation functions suitable for dynamic analysis are presented. Finally, we validate our approach through several numerical examples on FGMs with irregular shapes and a variety of boundary conditions. From numerical experiments, the predicted results with PIDL demonstrate well agreement with analytical solutions and other numerical methods in predicting of both temperature and flux distributions and can be adaptive to transient analysis of FGMs with different shapes, which can be the promising surrogate model in transient dynamic analysis. KW - Wärmeübergang KW - Deep Learning KW - Modellierung KW - physics-informed activation function KW - heat transfer KW - functionally graded materials Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20230517-63666 UR - https://link.springer.com/article/10.1007/s00466-023-02287-x VL - 2023 SP - 1 EP - 12 PB - Springer CY - Berlin ER - TY - JOUR A1 - Guo, Hongwei A1 - Zhuang, Xiaoying A1 - Chen, Pengwan A1 - Alajlan, Naif A1 - Rabczuk, Timon T1 - Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis JF - Engineering with Computers N2 - 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 network with material transfer learning reducing the solution of the non-homogeneous partial differential equations to an optimization problem. We tested different configurations of the physics-informed neural network including smooth activation functions, sampling methods for collocation points generation and combined optimizers. A material transfer learning technique is utilized for non-homogeneous media with different material gradations and parameters, which enhance the generality and robustness of the proposed method. In order to identify the most influential parameters of the network configuration, we carried out a global sensitivity analysis. Finally, we provide a convergence proof of our DCM. The approach is validated through several benchmark problems, also testing different material variations. KW - Deep learning KW - Kollokationsmethode KW - Collocation method KW - Potential problem KW - Activation function KW - Transfer learning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220811-46764 UR - https://link.springer.com/article/10.1007/s00366-022-01633-6 VL - 2022 SP - 1 EP - 22 ER - TY - JOUR A1 - Jiang, Jin-Wu A1 - Zhuang, Xiaoying A1 - Rabczuk, Timon T1 - Orientation dependent thermal conductance in single-layer MoS 2 JF - Scientific Reports N2 - We investigate the thermal conductivity in the armchair and zigzag MoS2 nanoribbons, by combining the non-equilibrium Green's function approach and the first-principles method. A strong orientation dependence is observed in the thermal conductivity. Particularly, the thermal conductivity for the armchair MoS2 nanoribbon is about 673.6 Wm−1 K−1 in the armchair nanoribbon, and 841.1 Wm−1 K−1 in the zigzag nanoribbon at room temperature. By calculating the Caroli transmission, we disclose the underlying mechanism for this strong orientation dependence to be the fewer phonon transport channels in the armchair MoS2 nanoribbon in the frequency range of [150, 200] cm−1. Through the scaling of the phonon dispersion, we further illustrate that the thermal conductivity calculated for the MoS2 nanoribbon is esentially in consistent with the superior thermal conductivity found for graphene. KW - Mechanische Eigenschaft KW - Wärmeleitfähigkeit KW - Nanoribbons, thermal conductivity Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170418-31417 ER - TY - JOUR A1 - Nanthakumar, S.S. A1 - Lahmer, Tom A1 - Zhuang, Xiaoying A1 - Park, Harold S. A1 - Rabczuk, Timon T1 - Topology optimization of piezoelectric nanostructures JF - Journal of the Mechanics and Physics of Solids N2 - Topology optimization of piezoelectric nanostructures KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2016 SP - 316 EP - 335 ER -