Recent advances and applications of machine learning in experimental solid mechanics: A review
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 …
Laplace neural operator for solving differential equations
Neural operators map multiple functions to different functions, possibly in different spaces,
unlike standard neural networks. Hence, neural operators allow the solution of parametric …
unlike standard neural networks. Hence, neural operators allow the solution of parametric …
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 …
Deep learning in computational mechanics: a review
The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
Synergistic learning with multi-task deeponet for efficient pde problem solving
Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful
information from multiple tasks to improve generalization performance compared to single …
information from multiple tasks to improve generalization performance compared to single …
Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators
We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms
with parametric source positions, which have applications in virtual/augmented reality, game …
with parametric source positions, which have applications in virtual/augmented reality, game …
Separable deeponet: Breaking the curse of dimensionality in physics-informed machine learning
The deep operator network (DeepONet) is a popular neural operator architecture that has
shown promise in solving partial differential equations (PDEs) by using deep neural …
shown promise in solving partial differential equations (PDEs) by using deep neural …
Deep neural operators can predict the real-time response of floating offshore structures under irregular waves
The utilization of neural operators in a digital twin model of an offshore floating structure
holds the potential for a significant shift in the prediction of structural responses and health …
holds the potential for a significant shift in the prediction of structural responses and health …
Learning in latent spaces improves the predictive accuracy of deep neural operators
Operator regression provides a powerful means of constructing discretization-invariant
emulators for partial-differential equations (PDEs) describing physical systems. Neural …
emulators for partial-differential equations (PDEs) describing physical systems. Neural …
Neural operator learning for long-time integration in dynamical systems with recurrent neural networks
Deep neural networks are an attractive alternative for simulating complex dynamical
systems, as in comparison to traditional scientific computing methods, they offer reduced …
systems, as in comparison to traditional scientific computing methods, they offer reduced …