ZhaoChenCivilSciML / EQDiscovery-1
Physics-informed learning of governing equations from scarce data
☆11Updated 4 years ago
Alternatives and similar repositories for EQDiscovery-1:
Users that are interested in EQDiscovery-1 are comparing it to the libraries listed below
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆25Updated 3 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆67Updated 2 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆48Updated 4 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆69Updated last year
- Multifidelity DeepONet☆30Updated last year
- About Code release for “RoPINN: Region Optimized Physics-Informed Neural Networks” (NeurIPS 2024), https://arxiv.org/abs/2405.14369☆48Updated 4 months ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆20Updated 2 years ago
- Dimensionless learning codes for our paper called "Data-driven discovery of dimensionless numbers and governing laws from scarce measurem…☆36Updated 9 months ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to…☆47Updated 2 years ago
- ☆18Updated last year
- 🌌 Applications of Physics-Informed ML: A collection of notebooks from my Masters research, exploring how machine learning can solve scie…☆11Updated 4 months ago
- [ICLR 2024] Scaling physics-informed hard constraints with mixture-of-experts.☆31Updated 9 months ago
- Boosting the training of physics informed neural networks with transfer learning☆26Updated 3 years ago
- Tensoflow 2 implementation of physics informed deep learning.☆27Updated 4 years ago
- A toolbox for Sequential Bayesian Inference in uncertain nonlinear dynamic systems.☆11Updated 11 months ago
- Multi-fidelity regression with neural networks☆11Updated 4 months ago
- Encoding physics to learn reaction-diffusion processes☆95Updated last year
- Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication☆17Updated 3 years ago
- Multi-fidelity reduced-order surrogate modeling☆19Updated 3 months ago
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆58Updated 8 months ago
- Physics Informed Neural Networks (PINNs) + SPINNs + HyperPINNs with JAX 📓 Check out our various notebooks to get started ⚠️ Mirror repos…☆27Updated this week
- Physics-Informed Neural Networks Trained with Particle Swarm Optimization☆19Updated 2 years ago
- Physics-guided Neural Networks (PGNN) : An Application In Lake Temperature Modelling☆107Updated 3 years ago
- Sparse Physics-based and Interpretable Neural Networks☆48Updated 3 years ago
- Tutorials for Physics-Informed Neural Networks☆48Updated 10 months ago
- ☆13Updated 4 months ago
- ☆21Updated 3 years ago
- ☆28Updated last year
- Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10…☆24Updated last year
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆24Updated last year