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08/24/2023
Learning Only On Boundaries: a Physics-Informed Neural operator for Solving Parametric Partial Differential Equations in Complex Geometries
Recently deep learning surrogates and neural operators have shown promis...
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08/16/2023
An Expert's Guide to Training Physics-informed Neural Networks
Physics-informed neural networks (PINNs) have been popularized as a deep...
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05/18/2023
PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems
We develop a tool, which we name Protoplanetary Disk Operator Network (P...
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02/25/2023
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach
While the popularity of physics-informed neural networks (PINNs) is stea...
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10/03/2022
Random Weight Factorization Improves the Training of Continuous Neural Representations
Continuous neural representations have recently emerged as a powerful an...
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07/05/2022
Rethinking the Importance of Sampling in Physics-informed Neural Networks
Physics-informed neural networks (PINNs) have emerged as a powerful tool...
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03/14/2022
Respecting causality is all you need for training physics-informed neural networks
While the popularity of physics-informed neural networks (PINNs) is stea...
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10/25/2021
Fast PDE-constrained optimization via self-supervised operator learning
Design and optimal control problems are among the fundamental, ubiquitou...
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10/04/2021
Improved architectures and training algorithms for deep operator networks
Operator learning techniques have recently emerged as a powerful tool fo...
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06/09/2021
Long-time integration of parametric evolution equations with physics-informed DeepONets
Ordinary and partial differential equations (ODEs/PDEs) play a paramount...
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03/19/2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets
Deep operator networks (DeepONets) are receiving increased attention tha...
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12/18/2020
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
Physics-informed neural networks (PINNs) are demonstrating remarkable pr...
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07/28/2020
When and why PINNs fail to train: A neural tangent kernel perspective
Physics-informed neural networks (PINNs) have lately received great atte...
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06/04/2020
Deep learning of free boundary and Stefan problems
Free boundary problems appear naturally in numerous areas of mathematics...
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01/13/2020
Understanding and mitigating gradient pathologies in physics-informed neural networks
The widespread use of neural networks across different scientific domain...
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08/12/2019
An overlapping-free leaf segmentation method for plant point clouds
Automatic leaf segmentation, as well as identification and classificatio...
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07/02/2019