pde
Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators
Our code is available at https://github.com/sail-sg/stde
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Convolutional Neural Operators for robust and accurate learning of PDEs
Here, we present novel adaptations for convolutional neural networks to demonstrate that they are indeed able to process functions as inputs and outputs.
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PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs
Partial Differential Equations (PDEs) is still lacking. This study introduces PIN-Nacle, a benchmarking tool designed to fill this gap.
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7f970edb14104b81e70e3b03e1f5214f-Paper-Conference.pdf
To address this issue, some researchers [3,35,18]havetried toembed BCs into the ansatz.
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SurprisingInstabilitiesinTrainingDeepNetworksand aTheoreticalAnalysis
Learning rates and schedules are often chosen heuristically to address this trade-off.
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Deep Equilibrium Based Neural Operators for Steady-State PDEs
Data-driven machine learning approaches are being increasingly used to solve partial differential equations (PDEs).
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