Recent advances and applications of machine learning in experimental solid mechanics: A review
H Jin, E Zhang, HD Espinosa - Applied …, 2023 - asmedigitalcollection.asme.org
For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …
and understanding the mechanical properties of natural and novel artificial materials …
Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications
H Hu, L Qi, X Chao - Thin-Walled Structures, 2024 - Elsevier
For solving the computational solid mechanics problems, despite significant advances have
been achieved through the numerical discretization of partial differential equations (PDEs) …
been achieved through the numerical discretization of partial differential equations (PDEs) …
Physics-informed deep neural operator networks
Standard neural networks can approximate general nonlinear operators, represented either
explicitly by a combination of mathematical operators, eg in an advection–diffusion reaction …
explicitly by a combination of mathematical operators, eg in an advection–diffusion reaction …
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Deep neural operators can learn nonlinear mappings between infinite-dimensional function
spaces via deep neural networks. As promising surrogate solvers of partial differential …
spaces via deep neural networks. As promising surrogate solvers of partial differential …
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
Deep neural operators can learn operators mapping between infinite-dimensional function
spaces via deep neural networks and have become an emerging paradigm of scientific …
spaces via deep neural networks and have become an emerging paradigm of scientific …
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: Comparison with finite element …
Physics informed neural networks (PINNs) are capable of finding the solution for a given
boundary value problem. Here, the training of the network is equivalent to the minimization …
boundary value problem. Here, the training of the network is equivalent to the minimization …
Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration
Geologic carbon sequestration (GCS) is a safety-critical technology that aims to reduce the
amount of carbon dioxide in the atmosphere, which also places high demands on reliability …
amount of carbon dioxide in the atmosphere, which also places high demands on reliability …
Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads
A novel deep operator network (DeepONet) with a residual U-Net (ResUNet) as the trunk
network is devised to predict full-field highly nonlinear elastic–plastic stress response for …
network is devised to predict full-field highly nonlinear elastic–plastic stress response for …
Blending neural operators and relaxation methods in PDE numerical solvers
Neural networks suffer from spectral bias and have difficulty representing the high-frequency
components of a function, whereas relaxation methods can resolve high frequencies …
components of a function, whereas relaxation methods can resolve high frequencies …
Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling
Constitutive modeling based on continuum mechanics theory has been a classical approach
for modeling the mechanical responses of materials. However, when constitutive laws are …
for modeling the mechanical responses of materials. However, when constitutive laws are …