2022
DOI: 10.48550/arxiv.2210.05837
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

A composable machine-learning approach for steady-state simulations on high-resolution grids

Abstract: In this paper we show that our Machine Learning (ML) approach, CoMLSim (Composable Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher accuracy and generalization to out-of-distribution source terms and geometries than traditional ML baselines. Our unique approach combines key principles of traditional PDE solvers with local-learning and low-dimensional manifold techniques to iteratively simulate PDEs on large computational domains. The proposed approach is validated on more tha… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2022
2022
2022
2022

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 58 publications
0
1
0
Order By: Relevance
“…These methods provide accurate predictions, but they are computationally very expensive. As a result, researchers in the deep learning community have devised many different models to learn physics behind these engineering problems using supervised learning methods, that determine the input to output mapping [1,2,3,4,5] or unsupervised learning methods, that embed physical laws into loss functions to compute PDE solutions [6,7,8,9]. These physics-informed methods provide a unique benefit over most approaches by imposing initial and boundary conditions in the optimization process.…”
Section: Introductionmentioning
confidence: 99%
“…These methods provide accurate predictions, but they are computationally very expensive. As a result, researchers in the deep learning community have devised many different models to learn physics behind these engineering problems using supervised learning methods, that determine the input to output mapping [1,2,3,4,5] or unsupervised learning methods, that embed physical laws into loss functions to compute PDE solutions [6,7,8,9]. These physics-informed methods provide a unique benefit over most approaches by imposing initial and boundary conditions in the optimization process.…”
Section: Introductionmentioning
confidence: 99%