PredictiveIntelligenceLab / UQPINNsLinks
☆63Updated 6 years ago
Alternatives and similar repositories for UQPINNs
Users that are interested in UQPINNs are comparing it to the libraries listed below
Sorting:
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆89Updated last year
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 5 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆87Updated 4 years ago
- POD-PINN code and manuscript☆52Updated 8 months ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆150Updated 5 years ago
- Sparse Physics-based and Interpretable Neural Networks☆50Updated 3 years ago
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆41Updated 2 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆80Updated 3 years ago
- Pytorch implementation of Bayesian physics-informed neural networks☆60Updated 3 years ago
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification☆106Updated 5 years ago
- Multi-fidelity reduced-order surrogate modeling☆24Updated last month
- ☆25Updated 7 years ago
- Deep learning library for solving differential equations on top of PyTorch.☆61Updated 5 years ago
- ☆145Updated 3 years ago
- ☆21Updated 4 years ago
- ☆37Updated last year
- ☆97Updated 3 years ago
- ☆54Updated 2 years ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 2 years ago
- ☆64Updated 2 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆72Updated 2 years ago
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆49Updated 3 years ago
- Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10…☆25Updated last year
- Hidden physics models: Machine learning of nonlinear partial differential equations☆146Updated 5 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆74Updated 3 years ago
- ☆116Updated 6 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆27Updated 2 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆20Updated 2 years ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆32Updated last year