physics-informed neural network
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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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df438e5206f31600e6ae4af72f2725f1-Paper.pdf
Recent work in scientific machine learning has developed so-called physicsinformed neural network(PINN) models.
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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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Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks
PINNs are, however, sharing the same weakness with coordinate-based MLPs (or INRs), which hinders the application of PINNs/INRs to more diverse applications; for a new data instance (e.g., a new PDE for PINNs or a new image for INRs), training a new neural network (typically from
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