Data-driven methods for flow and transport in porous media: a review
This review focuses on recent advancements in data-driven methods for analyzing flow and
transport in porous media, which are showing promising potential for applications in energy …
transport in porous media, which are showing promising potential for applications in energy …
Ai foundation models for weather and climate: Applications, design, and implementation
SK Mukkavilli, DS Civitarese, J Schmude… - ar** foundational neural Partial
Differential Equation (PDE) solvers and neural operators through large-scale pretraining …
Differential Equation (PDE) solvers and neural operators through large-scale pretraining …
Building flexible machine learning models for scientific computing at scale
Foundation models have revolutionized language modeling, while whether this success is
replicated in scientific computing remains unexplored. We present OmniArch, the first …
replicated in scientific computing remains unexplored. We present OmniArch, the first …
A comparison of single and double generator formalisms for thermodynamics-informed neural networks
The development of inductive biases has been shown to be a very effective way to increase
the accuracy and robustness of neural networks, particularly when they are used to predict …
the accuracy and robustness of neural networks, particularly when they are used to predict …
Unitary convolutions for learning on graphs and groups
Data with geometric structure is ubiquitous in machine learning often arising from
fundamental symmetries in a domain, such as permutation-invariance in graphs and …
fundamental symmetries in a domain, such as permutation-invariance in graphs and …
[PDF][PDF] Attention-enhanced neural differential equations for physics-informed deep learning of ion transport
Species transport models typically combine partial differential equations (PDEs) with
relations from hindered transport theory to quantify electromigrative, convective, and …
relations from hindered transport theory to quantify electromigrative, convective, and …
Physics and Lie symmetry informed Gaussian processes
D Dalton, D Husmeier, H Gao - Forty-first International Conference …, 2024 - openreview.net
Physics-informed machine learning (PIML) has established itself as a new scientific
paradigm which enables the seamless integration of observational data with partial …
paradigm which enables the seamless integration of observational data with partial …