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Saad Qadeer, Panos Stinis, Hui Wan
Abstract
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10.1017/s0962492902000077 · doi-reference
Multi-stage neural networks: Function approximator of machine precision
10.1016/j.jcp.2024.112865 · doi-reference
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10.1016/j.jcp.2007.04.014 · doi-reference
Problem reduction, renormalization, and memory
10.2140/camcos.2006.1.1 · doi-reference
Optimal prediction with memory
10.1016/s0167-2789(02)00446-3 · doi-reference
Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher re via transfer learning
10.1016/j.jcp.2022.111090 · doi-reference
An ensemble of neural networks for moist physics processes, its generalizability and stable integration
10.1029/2022ms003508 · doi-reference
Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows
10.1103/physrevfluids.6.050501 · doi-reference
Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
10.1038/s41467-020-17142-3 · doi-reference
Deep neural networks for data-driven LES closure models
10.1016/j.jcp.2019.108910 · doi-reference
Deep learning to represent subgrid processes in climate models
10.1073/pnas.1810286115 · doi-reference
A multifidelity deep operator network approach to closure for multiscale systems
10.1016/j.cma.2023.116161 · doi-reference
On closures for reduced order models—A spectrum of first-principle to machine-learned avenues
10.1063/5.0061577 · doi-reference
Data-driven equation discovery of ocean mesoscale closures
10.1029/2020gl088376 · doi-reference
Generalizable physics-constrained modeling using learning and inference assisted by feature-space engineering
10.1103/physrevfluids.6.124602 · doi-reference
Learning partial differential equations via data discovery and sparse optimization
10.1098/rspa.2016.0446 · doi-reference
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
10.1038/s42256-021-00302-5 · doi-reference
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
10.1109/72.392253 · doi-reference
Approximation capabilities of multilayer feedforward networks
10.1016/0893-6080(91)90009-t · doi-reference
Approximation by superpositions of a sigmoidal function
10.1007/bf02551274 · doi-reference