PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
Recently, the advent of deep learning has spurred interest in the development of physics-
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …
Numerical solution of inverse problems by weak adversarial networks
In this paper, a weak adversarial network approach is developed to numerically solve a
class of inverse problems, including electrical impedance tomography and dynamic …
class of inverse problems, including electrical impedance tomography and dynamic …
Generative adversarial reduced order modelling
In this work, we present GAROM, a new approach for reduced order modeling (ROM) based
on generative adversarial networks (GANs). GANs attempt to learn to generate data with the …
on generative adversarial networks (GANs). GANs attempt to learn to generate data with the …
Application of boundary-fitted convolutional neural network to simulate non-Newtonian fluid flow behavior in eccentric annulus
Mathematical simulation of non-Newtonian fluid flow is an enduring problem with imperative
influence on numerous industrial processes such as oil and gas drilling, food processing …
influence on numerous industrial processes such as oil and gas drilling, food processing …
[HTML][HTML] Monte carlo simulation of SDES using GANS
J van Rhijn, CW Oosterlee, LA Grzelak… - Japan Journal of Industrial …, 2023 - Springer
Generative adversarial networks (GANs) have shown promising results when applied on
partial differential equations and financial time series generation. We investigate if GANs …
partial differential equations and financial time series generation. We investigate if GANs …
Deep generative models that solve pdes: Distributed computing for training large data-free models
Recent progress in scientific machine learning (SciML) has opened up the possibility of
training novel neural network architectures that solve complex partial differential equations …
training novel neural network architectures that solve complex partial differential equations …
Generative downscaling of PDE solvers with physics-guided diffusion models
Solving partial differential equations (PDEs) on fine spatio-temporal scales for high-fidelity
solutions is critical for numerous scientific breakthroughs. Yet, this process can be …
solutions is critical for numerous scientific breakthroughs. Yet, this process can be …