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 4 months ago
Alternatives and similar repositories for DINN
Users that are interested in DINN are comparing it to the libraries listed below
Sorting:
- This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means…☆179Updated 4 years ago
 - ☆195Updated 7 months ago
 - ☆116Updated 6 years ago
 - A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software☆71Updated last year
 - ☆373Updated 4 years ago
 - ETH Zürich Deep Learning in Scientific Computing Master's course 2023☆120Updated last year
 - Neural parameter calibration for multi-agent models. Uses neural networks to estimate marginal densities on parameters and networks☆31Updated this week
 - Official repository for the paper "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery"☆77Updated 2 years ago
 - PINN-COVID analyzes a plurality of epidemiological models through the lens of physics-informed neural networks (PINNs).☆21Updated 4 years ago
 - Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆59Updated 3 years ago
 - Playing around with Phyiscs-Informed Neural Networks☆94Updated 3 months ago
 - ☆36Updated 3 years ago
 - ME 539 - Introduction to Scientific Machine Learning☆122Updated 2 months ago
 - ☆23Updated 4 months ago
 - ☆265Updated 2 years ago
 - Code accompanying my blog post: So, what is a physics-informed neural network?☆649Updated 3 years ago
 - ☆14Updated last year
 - Bayesian neural networks via MCMC: tutorial☆58Updated last year
 - Using graph network to solve PDEs☆419Updated 5 months ago
 - Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high perf…☆233Updated 2 years ago
 - OSS library that implements deep learning methods for partial differential equations and much more☆454Updated last month
 - Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems☆146Updated this week
 - Physics-informed learning of governing equations from scarce data☆156Updated 2 years ago
 - Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]☆254Updated 4 years ago
 - ☆28Updated 4 years ago
 - A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, i…☆766Updated 3 months ago
 - ☆42Updated 5 years ago
 - ☆58Updated last year
 - ☆211Updated last year
 - Characterizing possible failure modes in physics-informed neural networks.☆143Updated 3 years ago