dynamicslab / dominant-balance
Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)
☆55Updated 4 years ago
Alternatives and similar repositories for dominant-balance:
Users that are interested in dominant-balance are comparing it to the libraries listed below
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆67Updated 4 years ago
- Easy Reduced Basis method☆83Updated 3 months ago
- ☆116Updated 5 years ago
- A library of tools for computing variants of Dynamic Mode Decomposition☆46Updated 7 years ago
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆62Updated 4 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆47Updated 4 years ago
- Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.☆69Updated this week
- ☆88Updated 3 years ago
- Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" …☆29Updated 2 years ago
- A library for dimensionality reduction on spatial-temporal PDE☆62Updated 9 months ago
- ATHENA: Advanced Techniques for High dimensional parameter spaces to Enhance Numerical Analysis☆51Updated last year
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆54Updated 2 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆91Updated 2 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆24Updated 3 years ago
- ☆62Updated 5 years ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to…☆44Updated 2 years ago
- combination of sparse identification of nonlinear dynamics with Akaike information criteria☆16Updated 7 years ago
- SINDy (Sparse Identification of Nonlinear Dynamics) algorithms☆71Updated 2 years ago
- Repository from the paper https://arxiv.org/abs/1908.04127, to train Deep Reinforcement Learning in Fluid Mechanics Setup.☆62Updated 3 years ago
- ☆40Updated 4 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆143Updated 4 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆62Updated 2 years ago
- hPINN: Physics-informed neural networks with hard constraints☆122Updated 3 years ago
- ☆187Updated 3 years ago
- Update PDEKoopman code to Tensorflow 2☆22Updated 3 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆83Updated 4 years ago
- Deep learning framework for model reduction of dynamical systems☆21Updated 4 years ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆23Updated 3 years ago
- A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks. arXiv:1804.09269☆41Updated 6 years ago
- Codes for Linear and Nonlinear Disambiguation Optimization (LANDO)☆27Updated 3 years ago