EdWangLoDaSc/DSOVT
Sample codes for training of dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraints (DSOVT) by Wang et al.
This project helps oceanographers, meteorologists, or climate scientists predict complex environmental phenomena like sea surface temperatures or shallow water dynamics. It takes sparse, real-world observation data and uses deep learning, enhanced with physics rules, to generate highly accurate and efficient spatio-temporal predictions and rolling forecasts. This is designed for researchers or practitioners needing precise forecasting of dynamic natural systems.
No commits in the last 6 months.
Use this if you need to accurately predict how environmental systems evolve over time, especially when your observational data is incomplete or limited to specific locations.
Not ideal if you are looking for a simple, off-the-shelf forecasting tool that doesn't require familiarity with advanced deep learning or geostatistical methods.
Stars
6
Forks
—
Language
Jupyter Notebook
License
—
Category
Last pushed
Jun 11, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/EdWangLoDaSc/DSOVT"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
lululxvi/deepxde
A library for scientific machine learning and physics-informed learning
pnnl/neuromancer
Pytorch-based framework for solving parametric constrained optimization problems,...
wilsonrljr/sysidentpy
A Python Package For System Identification Using NARMAX Models
dynamicslab/pysindy
A package for the sparse identification of nonlinear dynamical systems from data
google-research/torchsde
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.