Data-driven techniques in rheology: Developments, Challenges and Perspective
With the rapid development and adoption of different data-driven techniques in rheology,
this review aims to reflect on the advent and growth of these frameworks, survey the state-of …
this review aims to reflect on the advent and growth of these frameworks, survey the state-of …
Identifying constitutive parameters for complex hyperelastic materials using physics-informed neural networks
S Song, H Jin - Soft Matter, 2024 - pubs.rsc.org
Identifying constitutive parameters in engineering and biological materials, particularly those
with intricate geometries and mechanical behaviors, remains a longstanding challenge. The …
with intricate geometries and mechanical behaviors, remains a longstanding challenge. The …
Mechanical metamaterials fabricated from self-assembly: A perspective
H Jin, HD Espinosa - Journal of Applied Mechanics, 2024 - asmedigitalcollection.asme.org
Mechanical metamaterials, whose unique mechanical properties stem from their structural
design rather than material constituents, are gaining popularity in engineering applications …
design rather than material constituents, are gaining popularity in engineering applications …
Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations
J Wang, Y Li, A Wu, Z Chen, J Huang, Q Wang, F Liu - Applied Sciences, 2024 - mdpi.com
This paper establishes a method for solving partial differential equations using a multi-step
physics-informed deep operator neural network. The network is trained by embedding …
physics-informed deep operator neural network. The network is trained by embedding …
Thermodynamically-Informed Iterative Neural Operators for Heterogeneous Elastic Localization
C Kelly, SR Kalidindi - arXiv preprint arXiv:2411.06529, 2024 - arxiv.org
Engineering problems frequently require solution of governing equations with spatially-
varying discontinuous coefficients. Even for linear elliptic problems, mapping large …
varying discontinuous coefficients. Even for linear elliptic problems, mapping large …
Separable Physics-Informed Neural Networks for the solution of elasticity problems
VA Es' kin, DV Davydov, JV Gur'eva… - arXiv preprint arXiv …, 2024 - arxiv.org
A method for solving elasticity problems based on separable physics-informed neural
networks (SPINN) in conjunction with the deep energy method (DEM) is presented …
networks (SPINN) in conjunction with the deep energy method (DEM) is presented …
[PDF][PDF] Investigating Time-Dependent Properties of Metamaterials for Enhanced Performance
N Davis, S Lopez, L Smith, O Miller, A Brown, E Wilson - researchgate.net
Metamaterials have emerged as a revolutionary class of materials characterized by unique
properties that are not typically found in nature. This study investigates the time-dependent …
properties that are not typically found in nature. This study investigates the time-dependent …