Code for paper "Physics-based machine learning for modeling IP3 induced calcium oscillations" - DOI: 10.5281/zenodo.4839127
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Updated
May 29, 2021 - Mathematica
Code for paper "Physics-based machine learning for modeling IP3 induced calcium oscillations" - DOI: 10.5281/zenodo.4839127
Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs
Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid flow dynamics
TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks (UQPINNs).
UQ Group (Director: Hadi Meidani)
TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).
Physics-informed information field theory - Solve inverse problems with built-in model form uncertainty estimation
Going through the tutorial on Physics-informed Neural Networks: https://github.com/madagra/basic-pinn
Dimensionless learning codes for our paper called "Data-driven discovery of dimensionless numbers and governing laws from scarce measurements".
SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance
Short review of Physics-Informed ML/DL
Physics-Informed Neural networks for Advanced modeling
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