Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations
In Computational Fluid Dynamics (CFD), coarse mesh simulations offer computational
efficiency but often lack precision. Applying conventional super-resolution to these …
efficiency but often lack precision. Applying conventional super-resolution to these …
DiffGrad for Physics-Informed Neural Networks
JU Rahman - arXiv preprint arXiv:2409.03239, 2024 - arxiv.org
Physics-Informed Neural Networks (PINNs) are regarded as state-of-the-art tools for
addressing highly nonlinear problems based on partial differential equations. Despite their …
addressing highly nonlinear problems based on partial differential equations. Despite their …
Equation identification for fluid flows via physics-informed neural networks
A New, M Villafañe-Delgado, C Shugert - arXiv preprint arXiv:2408.17271, 2024 - arxiv.org
Scientific machine learning (SciML) methods such as physics-informed neural networks
(PINNs) are used to estimate parameters of interest from governing equations and small …
(PINNs) are used to estimate parameters of interest from governing equations and small …
Deep Learning for Studying Materials Stability and Solving Thermodynamically Consistent PDES With Dynamic Boundary Conditions in Arbitrary Domains
C Li - 2023 - scholarcommons.sc.edu
Deep learning has achieved remarkable success in various fields, including image
processing, natural language processing, and signal processing, ushering in a …
processing, natural language processing, and signal processing, ushering in a …