Shaier / DINNLinks
Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).
☆33Updated 3 months ago
Alternatives and similar repositories for DINN
Users that are interested in DINN are comparing it to the libraries listed below
Sorting:
- ☆189Updated 5 months ago
- This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means…☆178Updated 4 years ago
- ☆36Updated 3 years ago
- Bayesian neural networks via MCMC: tutorial☆58Updated 10 months ago
- ☆116Updated 6 years ago
- Playing around with Phyiscs-Informed Neural Networks☆89Updated 2 months ago
- physics-guided neural networks (phygnn)☆96Updated 2 weeks ago
- Hamiltonian Neural Networks for solving Differential Equations☆22Updated 3 years ago
- ☆42Updated 5 years ago
- Official repository for the paper "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery"☆76Updated 2 years ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆56Updated 3 years ago
- A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software☆64Updated last year
- ☆259Updated 2 years ago
- ME 539 - Introduction to Scientific Machine Learning☆122Updated 2 weeks ago
- Neural parameter calibration for multi-agent models. Uses neural networks to estimate marginal densities on parameters and networks☆31Updated 2 weeks ago
- Basic implementation of physics-informed neural networks for solving differential equations☆94Updated 8 months ago
- Physics-informed neural networks package☆323Updated 3 years ago
- ☆28Updated 4 years ago
- SINDy (Sparse Identification of Nonlinear Dynamics) algorithms☆79Updated 2 years ago
- Physics-informed learning of governing equations from scarce data☆150Updated 2 years ago
- ETH Zürich Deep Learning in Scientific Computing Master's course 2023☆116Updated last year
- ☆205Updated last year
- ☆23Updated 2 months ago
- Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".☆37Updated 3 years ago
- ☆222Updated 3 years ago
- TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).☆262Updated last year
- PINN-COVID analyzes a plurality of epidemiological models through the lens of physics-informed neural networks (PINNs).☆20Updated 3 years ago
- ☆14Updated 11 months ago
- Python codes for Introduction to Computational Stochastic PDE☆44Updated 7 months ago
- ☆57Updated last year