tensordiffeq / TensorDiffEq
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
☆113Updated 3 years ago
Alternatives and similar repositories for TensorDiffEq:
Users that are interested in TensorDiffEq are comparing it to the libraries listed below
- Example problems in Physics informed neural network in JAX☆80Updated last year
- ☆116Updated 5 years ago
- ☆92Updated 3 years ago
- Solving PDEs with NNs☆53Updated 2 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆67Updated 4 years ago
- Neural network based solvers for partial differential equations and inverse problems . Implementation of physics-informed neural networks…☆149Updated 3 months ago
- ☆177Updated 3 weeks ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆190Updated 2 years ago
- ☆200Updated 3 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆145Updated 5 years ago
- ☆134Updated 2 years ago
- Characterizing possible failure modes in physics-informed neural networks.☆134Updated 3 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆147Updated 5 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆92Updated 2 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆84Updated 4 years ago
- Applications of PINOs☆122Updated 2 years ago
- ☆51Updated 2 years ago
- PyTorch-FEniCS interface☆100Updated 4 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆69Updated 2 years ago
- Code for the paper "Poseidon: Efficient Foundation Models for PDEs"☆139Updated last week
- Code for the paper "Thermodynamics-informed graph neural networks" published in IEEE Transactions on Artificial Intelligence (TAI).☆98Updated 8 months ago
- ☆62Updated 5 years ago
- Learning Green's functions of partial differential equations with deep learning.☆65Updated last year
- Dimension reduced surrogate construction for parametric PDE maps☆37Updated 3 weeks ago
- ☆107Updated 2 months ago
- Differentiable interface to FEniCS for JAX☆53Updated 3 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆87Updated last year
- hPINN: Physics-informed neural networks with hard constraints☆132Updated 3 years ago
- ☆41Updated 4 years ago
- ☆161Updated last year