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Auto-Picnn: Automated Machine Learning for Physics-Informed Convolutional Neural Networks

https://doi.org/10.2139/ssrn.4803299
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inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-extracted
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/s00366-021-01586-2openalex-extracted
Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media
inferredcites →https://doi.org/10.1007/s00466-024-02554-5openalex-extracted
MRF-PINN: a multi-receptive-field convolutional physics-informed neural network for solving partial differential equations
inferredcites →https://doi.org/10.1007/s10915-022-01939-zopenalex-extracted
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
inferredcites →https://doi.org/10.1016/j.cma.2021.114399openalex-extracted
PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
inferredcites →https://doi.org/10.1016/j.cma.2022.114823openalex-extracted
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
inferredcites →https://doi.org/10.1016/j.cma.2022.114909openalex-extracted
CAN-PINN: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method
inferredcites →https://doi.org/10.1016/j.cma.2023.115944openalex-extracted
An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator
inferredcites →https://doi.org/10.1016/j.cma.2023.116139openalex-extracted
Automatic boundary fitting framework of boundary dependent physics-informed neural network solving partial differential equation with complex boundary conditions
inferredcites →https://doi.org/10.1016/j.engappai.2022.105516openalex-extracted
Physics-informed convolutional neural networks for temperature field prediction of heat source layout without labeled data
inferredcites →https://doi.org/10.1016/j.engstruct.2020.110704openalex-extracted
Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling
inferredcites →https://doi.org/10.1016/j.jcp.2018.08.029openalex-extracted
DGM: A deep learning algorithm for solving partial differential equations
inferredcites →https://doi.org/10.1016/j.jcp.2018.10.045openalex-extracted
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
inferredcites →https://doi.org/10.1016/j.jcp.2019.05.024openalex-extracted
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
inferredcites →https://doi.org/10.1016/j.jcp.2019.109056openalex-extracted
Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks
inferredcites →https://doi.org/10.1016/j.jcp.2020.110079openalex-extracted
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
inferredcites →https://doi.org/10.1016/j.jcp.2022.111722openalex-extracted
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
inferredcites →https://doi.org/10.1016/j.jcp.2023.111919openalex-extracted
A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media
inferredcites →https://doi.org/10.1016/j.jcp.2023.112603openalex-extracted
NAS-PINN: Neural architecture search-guided physics-informed neural network for solving PDEs
inferredcites →https://doi.org/10.1016/j.knosys.2020.106622openalex-extracted
AutoML: A survey of the state-of-the-art
inferredcites →https://doi.org/10.1017/9781009089517.019openalex-extracted
Physics-Informed Machine Learning
inferredcites →https://doi.org/10.1038/s42256-021-00302-5openalex-extracted
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
inferredcites →https://doi.org/10.1109/access.2019.2908991openalex-extracted
NAS-Unet: Neural Architecture Search for Medical Image Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-extracted
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-extracted
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/tnnls.2025.3545967openalex-extracted
Partial Differential Equations Meet Deep Neural Networks: A Survey
inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-extracted
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
inferredcites →https://doi.org/10.1109/tpami.2017.2699184openalex-extracted
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
inferredcites →https://doi.org/10.1137/19m1274067openalex-extracted
DeepXDE: A Deep Learning Library for Solving Differential Equations
inferredcites →https://doi.org/10.1145/3097983.3098043openalex-extracted
Google Vizier
inferredcites →https://doi.org/10.3389/fdata.2021.669097openalex-extracted
The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?
inferredcites →https://doi.org/10.48550/arxiv.1411.4038openalex-extracted
Fully Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1611.01578openalex-extracted
Neural Architecture Search with Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.1806.09055openalex-extracted
DARTS: Differentiable Architecture Search
inferredcites →https://doi.org/10.48550/arxiv.2002.00021openalex-extracted
Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence
inferredcites →https://doi.org/10.48550/arxiv.2010.08895openalex-extracted
Fourier Neural Operator for Parametric Partial Differential Equations
inferredcites →https://doi.org/10.48550/arxiv.2205.13748openalex-extracted
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture
inferredcites →https://doi.org/10.48550/arxiv.2212.04058openalex-extracted
AutoPINN: When AutoML Meets Physics-Informed Neural Networks
inferredcites →https://doi.org/10.52202/068431-0602openalex-extracted
Is L2 Physics Informed Loss Always Suitable for Training Physics Informed Neural Network?
inferredcites →https://doi.org/10.48550/arxiv.2010.12344openalex-snapshot
Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning
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