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uq-course
Introduction to Uncertainty Quantificationdata-analytics-se
ME 539 - Introduction to Scientific Machine Learningpysmc
Sequential Monte Carlo working on top of pymcpy-design
Design of random experiments in Pythonpy-mcmc
A simple MCMC framework for training Gaussian processes adding functionality to GPy.py-orthpol
Construct orhogonal polynomials using Pythoninverse-bgo
Use Bayesian Global Optimization to solve inverse problemsvariational-elliptic-SPDE
advanced-scientific-machine-learning
ME 697 - Advanced Scientific Machine Learningpift-paper-2023
Physics-informed information field theory - Solve inverse problems with built-in model form uncertainty estimationpy-bgo
An implementation Bayesian global optimization in Pythoncluster-opt-bgo
Bayesian Global Optimization for Minimum Energy Cluster Identificationpy-aspgp
paper-2023-inverse-map-karumuri
Code for the inverse map paperpaper-2023-strength-karumuri
pslab
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