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pro vyhledávání: '"Graham Brady"'
Neural operators (NOs) employ deep neural networks to learn mappings between infinite-dimensional function spaces. Deep operator network (DeepONet), a popular NO architecture, has demonstrated success in the real-time prediction of complex dynamics a
Externí odkaz:
http://arxiv.org/abs/2409.13280
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
Design and analysis of inelastic materials requires prediction of physical responses that evolve under loading. Numerical simulation of such behavior using finite element (FE) approaches can call for significant time and computational effort. To addr
Externí odkaz:
http://arxiv.org/abs/2310.19128
Microstructural heterogeneity affects the macro-scale behavior of materials. Conversely, load distribution at the macro-scale changes the microstructural response. These up-scaling and down-scaling relations are often modeled using multiscale finite
Externí odkaz:
http://arxiv.org/abs/2212.14601
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 December 2024 432 Part B
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 November 2024 431
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 January 2025 433 Part A
Autor:
D'Elia, Marta, Deng, Hang, Fraces, Cedric, Garikipati, Krishna, Graham-Brady, Lori, Howard, Amanda, Karniadakis, George, Keshavarzzadeh, Vahid, Kirby, Robert M., Kutz, Nathan, Li, Chunhui, Liu, Xing, Lu, Hannah, Newell, Pania, O'Malley, Daniel, Prodanovic, Masa, Srinivasan, Gowri, Tartakovsky, Alexandre, Tartakovsky, Daniel M., Tchelepi, Hamdi, Vazic, Bozo, Viswanathan, Hari, Yoon, Hongkyu, Zarzycki, Piotr
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learn
Externí odkaz:
http://arxiv.org/abs/2202.04137
Computational stress analysis is an important step in the design of material systems. Finite element method (FEM) is a standard approach of performing stress analysis of complex material systems. A way to accelerate stress analysis is to replace FEM
Externí odkaz:
http://arxiv.org/abs/2111.05271
Stochastic microstructure reconstruction involves digital generation of microstructures that match key statistics and characteristics of a (set of) target microstructure(s). This process enables computational analyses on ensembles of microstructures
Externí odkaz:
http://arxiv.org/abs/2102.02407