Predictive Science Laboratory

uq-course

Introduction to Uncertainty Quantification

data-analytics-se

ME 539 - Introduction to Scientific Machine Learning

pysmc

Sequential Monte Carlo working on top of pymc

py-design

Design of random experiments in Python

py-mcmc

A simple MCMC framework for training Gaussian processes adding functionality to GPy.

py-orthpol

Construct orhogonal polynomials using Python

inverse-bgo

Use Bayesian Global Optimization to solve inverse problems

variational-elliptic-SPDE

pift-paper-2023

Physics-informed information field theory - Solve inverse problems with built-in model form uncertainty estimation

py-bgo

An implementation Bayesian global optimization in Python

advanced-scientific-machine-learning

ME 697 - Advanced Scientific Machine Learning

cluster-opt-bgo

Bayesian Global Optimization for Minimum Energy Cluster Identification

py-aspgp

GPy

Gaussian processes framework in python

orthojax

Orthogonal polynomials (polynomial chaos) in Jax.

paper-2023-inverse-map-karumuri

Code for the inverse map paper

pyipopt

A Python connector to IPOPT

paper-2023-strength-karumuri

pslab

Documentation on the labs codes

pymc

PyMC: Bayesian Stochastic Modelling in Python

caffe

Caffe: a fast open framework for deep learning.

jaxpi

Forked version of jaxpi to be used in ME 697.

OrthogonalPolynomials.jl

Orthogonal polynomials with respect to arbitrary probability densities for Julia

www

www2

Predictive Science Laboratory - Website v2.0