Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Learning reduced-order models for cardiovascular simulations with graph neural networks
L Pegolotti, MR Pfaller, NL Rubio, K Ding… - Computers in Biology …, 2024 - Elsevier
Reduced-order models based on physics are a popular choice in cardiovascular modeling
due to their efficiency, but they may experience loss in accuracy when working with …
due to their efficiency, but they may experience loss in accuracy when working with …
Care: Modeling interacting dynamics under temporal environmental variation
Modeling interacting dynamical systems, such as fluid dynamics and intermolecular
interactions, is a fundamental research problem for understanding and simulating complex …
interactions, is a fundamental research problem for understanding and simulating complex …
Learning to accelerate partial differential equations via latent global evolution
Simulating the time evolution of Partial Differential Equations (PDEs) of large-scale systems
is crucial in many scientific and engineering domains such as fluid dynamics, weather …
is crucial in many scientific and engineering domains such as fluid dynamics, weather …
Graph neural networks for the prediction of aircraft surface pressure distributions
D Hines, P Bekemeyer - Aerospace Science and Technology, 2023 - Elsevier
Aircraft design requires a multitude of aerodynamic data and providing this solely based on
high-quality methods such as computational fluid dynamics is prohibitive from a cost and …
high-quality methods such as computational fluid dynamics is prohibitive from a cost and …
Deep hierarchical distillation proxy-oil modeling for heterogeneous carbonate reservoirs
This paper presents a novel few-shot proxy modeling approach for the oil and gas industry
to reduce reliance on numerical simulators for reservoir analysis. The strategy introduces a …
to reduce reliance on numerical simulators for reservoir analysis. The strategy introduces a …
Learning CO2 plume migration in faulted reservoirs with Graph Neural Networks
Deep-learning-based surrogate models provide an efficient complement to numerical
simulations for subsurface flow problems such as CO 2 geological storage. Accurately …
simulations for subsurface flow problems such as CO 2 geological storage. Accurately …
Cross-Domain Feature learning and data augmentation for few-shot proxy development in oil industry
In reservoir engineering, numerical simulators are crucial for analyzing risks and
uncertainties. The decision-making plan is complex due to numerous uncertain variables …
uncertainties. The decision-making plan is complex due to numerous uncertain variables …
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Geometric graph is a special kind of graph with geometric features, which is vital to model
many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical …
many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical …
Graph Network Surrogate Model for Optimizing the Placement of Horizontal Injection Wells for CO2 Storage
H Tang, LJ Durlofsky - arXiv preprint arXiv:2410.07142, 2024 - arxiv.org
Optimizing the locations of multiple CO2 injection wells will be essential as we proceed from
demonstration-scale to large-scale carbon storage operations. Well placement optimization …
demonstration-scale to large-scale carbon storage operations. Well placement optimization …