autoemulate

Simulations of physical systems are often slow and need lots of compute, which makes them unpractical for applications like digital twins, or in situations where they have to run thousands of times, like sensitivity analyses. The goal of autoemulate is to make it easy to replace simulations with fast, accurate emulators. To do this, autoemulate automatically fits and compares lots of models, like Radial Basis Functions, Gaussian Processes or Neural Networks to find the best emulator for a simulation.

The project is in very early development.

setup

using Poetry:

git clone https://github.com/alan-turing-institute/autoemulate.git
cd autoemulate
poetry install
poetry shell

quick start

import numpy as np
from autoemulate.compare import AutoEmulate
from autoemulate.experimental_design import LatinHypercube
from autoemulate.demos.projectile import simulator

# sample from a simulation
lhd = LatinHypercube([(-5., 1.), (0., 1000.)])
X = lhd.sample(100)
y = np.array([simulator(x) for x in X])

# compare emulator models
ae = AutoEmulate()
ae.setup(X, y)
ae.compare() 

# evaluate
ae.print_results()

# save & load best model
ae.save_model("best_model")
best_emulator = ae.load_model("best_model")

# emulate
best_emulator.predict(X)

Contributors

_{Kalle Westerling}
📖 💻 🖋

_{Bryan M. Li}
💻

_martin
💻 🤔 📖 🚧 🔬 👀

_{Eric Daub}
🤔 📆 👀 💻

_{steven niederer}
🤔 🖋 📆