ETH-WindMil / JSD-SBI
A toolbox for Sequential Bayesian Inference in uncertain nonlinear dynamic systems.
☆12Updated last year
Alternatives and similar repositories for JSD-SBI:
Users that are interested in JSD-SBI are comparing it to the libraries listed below
- ☆28Updated last year
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆69Updated 2 years ago
- Sonkyo blade benchmark☆18Updated 3 years ago
- mathLab mirror of Python Dynamic Mode Decomposition☆87Updated last month
- Research/development of physics-informed neural networks for dynamic systems☆20Updated 5 months ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆48Updated 4 years ago
- Data-driven reduced order modeling for nonlinear dynamical systems☆69Updated last week
- Empowering extended Kalman filter (EKF) with Sparse Identification of Nonlinear Dynamics (SINDy)☆49Updated 2 weeks ago
- Implementing a physics-informed DeepONet from scratch☆38Updated last year
- Multi-fidelity regression with neural networks☆12Updated 4 months ago
- ☆11Updated 11 months ago
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆25Updated 3 years ago
- We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.☆79Updated last year
- Tutorials for Physics-Informed Neural Networks☆56Updated 10 months ago
- Multi-fidelity reduced-order surrogate modeling☆21Updated 4 months ago
- ☆124Updated 2 years ago
- ☆13Updated 4 months ago
- Identification of Bouc–Wen type models using the transitional Markov chain Monte Carlo method☆11Updated last month
- This repository presents a series of analysis on the performance of Physics-Informed Neural Networks in vibrational systems. The limitati…☆12Updated 2 years ago
- Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".☆37Updated 2 years ago
- Physics-guided Deep Markov Models☆11Updated 2 years ago
- multi-fidelity neural network☆18Updated last year
- ☆67Updated last year
- An interpretable data-driven framework for building generative reduced order models with embedded uncertainty quantification☆32Updated last month
- This repository stores the implementation of three hysteretic models that use Physics-Guided neural networks (PGNNs) and Universal ordina…☆7Updated last year
- Discovers high dimensional models from 1D data using deep delay autoencoders☆34Updated 2 years ago
- ☆14Updated 3 years ago
- Enhancing Dynamic Mode Decomposition using Autoencoder Networks.☆29Updated 4 years ago
- Physics-guided Convolutional Neural Network☆67Updated 4 years ago
- Pytorch implementation of Bayesian physics-informed neural networks☆58Updated 3 years ago