Zobrazeno 1 - 10
of 1 690
pro vyhledávání: '"Shields, Michael"'
Autor:
Xu, Bin, Wu, Zhao, Lu, Jiayin, Shields, Michael D., Rycroft, Chris H., Bamer, Franz, Falk, Michael L.
This paper develops a general data-driven approach to stochastic elastoplastic modelling that leverages atomistic simulation data directly rather than by fitting parameters. The approach is developed in the context of metallic glasses, which present
Externí odkaz:
http://arxiv.org/abs/2410.00760
Autor:
Kumar, Varun, Goswami, Somdatta, Kontolati, Katiana, Shields, Michael D., Karniadakis, George Em
Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine l
Externí odkaz:
http://arxiv.org/abs/2408.02198
The Deep operator network (DeepONet) is a powerful yet simple neural operator architecture that utilizes two deep neural networks to learn mappings between infinite-dimensional function spaces. This architecture is highly flexible, allowing the evalu
Externí odkaz:
http://arxiv.org/abs/2407.13010
Stress and material deformation field predictions are among the most important tasks in computational mechanics. These predictions are typically made by solving the governing equations of continuum mechanics using finite element analysis, which can b
Externí odkaz:
http://arxiv.org/abs/2406.14838
Multifidelity modeling has been steadily gaining attention as a tool to address the problem of exorbitant model evaluation costs that makes the estimation of failure probabilities a significant computational challenge for complex real-world problems,
Externí odkaz:
http://arxiv.org/abs/2405.03834
We present a novel physics-constrained polynomial chaos expansion as a surrogate modeling method capable of performing both scientific machine learning (SciML) and uncertainty quantification (UQ) tasks. The proposed method possesses a unique capabili
Externí odkaz:
http://arxiv.org/abs/2402.15115
In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low
Externí odkaz:
http://arxiv.org/abs/2401.16683
Autor:
Thaler, Denny, Dhulipala, Somayajulu L. N., Bamer, Franz, Markert, Bernd, Shields, Michael D.
We present a new Subset Simulation approach using Hamiltonian neural network-based Monte Carlo sampling for reliability analysis. The proposed strategy combines the superior sampling of the Hamiltonian Monte Carlo method with computationally efficien
Externí odkaz:
http://arxiv.org/abs/2401.05244
Surrogate modeling of costly mathematical models representing physical systems is challenging since it is typically not possible to create a large experimental design. Thus, it is beneficial to constrain the approximation to adhere to the known physi
Externí odkaz:
http://arxiv.org/abs/2309.01697
Autor:
Chauhan, Mohit, Ojeda-Tuz, Mariel, Catarelli, Ryan, Gurley, Kurtis, Tsapetis, Dimitrios, Shields, Michael D.
This paper explores the application of active learning strategies to adaptively learn Sobol indices for global sensitivity analysis. We demonstrate that active learning for Sobol indices poses unique challenges due to the definition of the Sobol inde
Externí odkaz:
http://arxiv.org/abs/2308.14220