tomoleary/dino
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
This project helps scientists and engineers quickly understand how complex systems behave when parameters change, especially when dealing with many variables. It takes in descriptions of physical models and system parameters, then rapidly predicts how changes in these parameters will affect the system's derivatives or sensitivities. Researchers and practitioners in fields like computational physics or engineering design would use this to accelerate analysis and optimization.
No commits in the last 6 months.
Use this if you need to efficiently calculate and understand the impact of numerous input variables on the behavior of complex physical systems described by partial differential equations.
Not ideal if your problem does not involve high-dimensional parametric analysis of systems or if you primarily need to solve basic forward/inverse problems without a focus on derivative information.
Stars
18
Forks
2
Language
Python
License
LGPL-2.1
Category
Last pushed
Jan 09, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/tomoleary/dino"
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.