mhpi/generic_deltamodel
Generic framework for building differentiable models.
This framework helps scientists and researchers build predictive models by combining traditional physics-based equations with modern machine learning techniques. You input historical environmental data, and it outputs a highly accurate model that can predict outcomes like river flow or plant photosynthesis. It's designed for environmental scientists, hydrologists, and climate researchers who need to simulate complex natural processes.
Use this if you need to create models that merge established scientific formulas with the predictive power of neural networks for environmental forecasting.
Not ideal if your problem doesn't involve combining physics-based equations with machine learning, or if you don't work with large environmental datasets.
Stars
20
Forks
6
Language
Python
License
—
Category
Last pushed
Mar 05, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mhpi/generic_deltamodel"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
sbi-dev/sbi
sbi is a Python package for simulation-based inference, designed to meet the needs of both...
SMTorg/smt
Surrogate Modeling Toolbox
reservoirpy/reservoirpy
A simple and flexible code for Reservoir Computing architectures like Echo State Networks
GPflow/GPflow
Gaussian processes in TensorFlow
dswah/pyGAM
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python