filippo-masi/NICE
Neural integration for constitutive equations
This tool helps materials scientists and engineers automatically discover the constitutive equations of materials from limited experimental observations. It takes partial, noisy measurements of a material's state over time, like strain protocols, and outputs an accurate, consistent, and robust constitutive model. It is designed for researchers in materials science, mechanical engineering, and solid mechanics.
No commits in the last 6 months.
Use this if you need to derive constitutive models for new materials from small, incomplete, or noisy experimental datasets, especially when traditional methods are too slow or data-intensive.
Not ideal if you have perfect, extensive datasets and established constitutive models, or if you are not working with material mechanics and time-evolving material states.
Stars
13
Forks
1
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Dec 12, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/filippo-masi/NICE"
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.