MJfadeaway/DAS-2
Deep Adaptive Sampling for Surrogate Modeling Without Labeled Data
This tool helps scientists and engineers build faster, more accurate simulation models for complex physical systems. It takes the mathematical equations describing a system and its parameters, and efficiently generates a compact 'surrogate model' that predicts system behavior. This is ideal for researchers in computational science or engineering who develop and use simulations of physical phenomena.
No commits in the last 6 months.
Use this if you need to create accurate and efficient surrogate models for parametric differential equations, especially when traditional methods struggle with high dimensionality or low regularity, and you lack extensive pre-labeled data.
Not ideal if your problem doesn't involve parametric differential equations or if you require a simple, off-the-shelf solution without deep learning expertise.
Stars
9
Forks
1
Language
Python
License
Apache-2.0
Category
Last pushed
Nov 29, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/MJfadeaway/DAS-2"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
pdebench/PDEBench
PDEBench: An Extensive Benchmark for Scientific Machine Learning
tum-pbs/PhiFlow
A differentiable PDE solving framework for machine learning
ArnauMiro/pyLowOrder
High performance parallel reduced order Modelling library
lettucecfd/lettuce
Computational Fluid Dynamics based on PyTorch and the Lattice Boltzmann Method
peterdsharpe/NeuralFoil
NeuralFoil is a practical airfoil aerodynamics analysis tool using physics-informed machine...