Zobrazeno 1 - 10
of 193
pro vyhledávání: '"P Bouklas"'
Recently, the phase field method has been increasingly used for brittle fractures in soft materials like polymers, elastomers, and biological tissues. When considering finite deformations to account for the highly deformable nature of soft materials,
Externí odkaz:
http://arxiv.org/abs/2408.05162
Autor:
Padmanabha, Govinda Anantha, Fuhg, Jan Niklas, Safta, Cosmin, Jones, Reese E., Bouklas, Nikolaos
Most scientific machine learning (SciML) applications of neural networks involve hundreds to thousands of parameters, and hence, uncertainty quantification for such models is plagued by the curse of dimensionality. Using physical applications, we sho
Externí odkaz:
http://arxiv.org/abs/2407.00761
Autor:
Jones, Reese E., Hamel, Craig M., Bolintineanu, Dan, Johnson, Kyle, de Macedo, Robert Buarque, Fuhg, Jan, Bouklas, Nikolaos, Kramer, Sharlotte
When deformation gradients act on the scale of the microstructure of a part due to geometry and loading, spatial correlations and finite-size effects in simulation cells cannot be neglected. We propose a multiscale method that accounts for these effe
Externí odkaz:
http://arxiv.org/abs/2405.19082
Autor:
Fuhg, Jan Niklas, Padmanabha, Govinda Anantha, Bouklas, Nikolaos, Bahmani, Bahador, Sun, WaiChing, Vlassis, Nikolaos N., Flaschel, Moritz, Carrara, Pietro, De Lorenzis, Laura
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-dependent response of solids. Our objective is to provide an organized tax
Externí odkaz:
http://arxiv.org/abs/2405.03658
Autor:
van Wees, Lloyd, Shankar, Karthik, Fuhg, Jan N., Bouklas, Nikolaos, Shade, Paul, Obstalecki, Mark, Kasemer, Matthew
In this study, we present a methodology to predict the macroscopic yield surface of metals and metallic alloys with general crystallographic textures. In previous work, we have established the use of partially input convex neural networks (pICNN) as
Externí odkaz:
http://arxiv.org/abs/2404.03863
We present a comprehensive theoretical and computational model that explores the behavior of a thin hydrated film bonded to a non-hydrated / impermeable soft substrate in the context of surface and bulk elasticity coupled with surface diffusion kinet
Externí odkaz:
http://arxiv.org/abs/2403.06005
Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints and to overcome the challenging and time-consuming task of formulatin
Externí odkaz:
http://arxiv.org/abs/2310.03652
Data-driven constitutive modeling frameworks based on neural networks and classical representation theorems have recently gained considerable attention due to their ability to easily incorporate constitutive constraints and their excellent generaliza
Externí odkaz:
http://arxiv.org/abs/2308.11080
Cellular contractility, migration, and extracellular matrix (ECM) mechanics are critical for a wide range of biological processes including embryonic development, wound healing, tissue morphogenesis, and regeneration. Even though the distinct respons
Externí odkaz:
http://arxiv.org/abs/2308.02979
We propose a physics informed, neural network-based elasto-viscoplasticity (NN-EVP) constitutive modeling framework for predicting the flow response in metals as a function of underlying grain size. The developed NN-EVP algorithm is based on input co
Externí odkaz:
http://arxiv.org/abs/2307.04301