| no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
| 1 |
A dive into spectral inference networks: improved algorithms for self-supervised learning of continuous spectral representations
|
Wu, J.
|
|
|
44 |
7 |
p. 1199-1224
|
article
|
| 2 |
An artificial viscosity augmented physics-informed neural network for incompressible flow
|
He, Yichuan
|
|
|
44 |
7 |
p. 1101-1110
|
article
|
| 3 |
Deep convolutional Ritz method: parametric PDE surrogates without labeled data
|
Fuhg, J. N.
|
|
|
44 |
7 |
p. 1151-1174
|
article
|
| 4 |
Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics
|
Wu, W.
|
|
|
44 |
7 |
p. 1039-1068
|
article
|
| 5 |
Gaussian process hydrodynamics
|
Owhadi, H.
|
|
|
44 |
7 |
p. 1175-1198
|
article
|
| 6 |
Peri-Net-Pro: the neural processes with quantified uncertainty for crack patterns
|
Kim, M.
|
|
|
44 |
7 |
p. 1085-1100
|
article
|
| 7 |
Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving partial differential equations with sharp solutions
|
Mao, Zhiping
|
|
|
44 |
7 |
p. 1069-1084
|
article
|
| 8 |
Preface: machine-learning approaches for computational mechanics
|
Li, Z.
|
|
|
44 |
7 |
p. 1035-1038
|
article
|
| 9 |
Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures
|
You, H. Q.
|
|
|
44 |
7 |
p. 1125-1150
|
article
|
| 10 |
Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems
|
Meng, Xuhui
|
|
|
44 |
7 |
p. 1111-1124
|
article
|
Journal description