Ali's repositories
sequential_design
Sequential Experimental Design for Functional Response Experiments
B-PINNs
Pytorch implementation of Bayesian physics-informed neural networks
chemical_vae
Code for 10.1021/acscentsci.7b00572, now running on Keras 2.0 and Tensorflow
Design_for_Copula
Bayesian sequential design for Copula models
ekfukf
EKF/UKF toolbox for Matlab/Octave
experiment-design
自己写的最优实验设计程序包（model based design of experiment for state-delay differential syustem) 。运行Main_case_4.m即可
hado-sim
An Bayesian optimal experimental design framework to discriminate between active learning models in Cognitive Science
harmonic-oscillator-pinn
Code accompanying my blog post: So, what is a physics-informed neural network?
iCardio
Code associated with the iCardio publication
latent_ode
Code for "Latent ODEs for Irregularly-Sampled Time Series" paper
Learning-Based-MPC
Learning-Based Model Predictive Control (LBMPC)
lwpls
Locally-Weighted Partial Least Squares (LWPLS)
mars
Discovering novel cell types across heterogenous single-cell experiments
optimal-design
Code to compute Optimal Experimental Design as in Balietti, Klein & Riedl (2020)
Particle_Filter
MATLAB implementation of standard particle filter, auxiliary particle filter, mixture particle filter, and out-of-sequence particle filter for an application to terrain-referenced navigation.
pytorch-domain-adaptation
A collection of implementations of adversarial domain adaptation algorithms
SCOT
Gromov-Wasserstein based optimal transport for aligning single-cell multi-omics data
sls
Implements stochastic line search
t-cell-relation-extraction
Literature mining for T cell relations
Teaching_Optimal_Transport
Repo for the course "Optimal Transport" at ENSAE Paris.
training-material
A collection of Galaxy-related training material
uot
The implementation for the paper "On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm".
xPINNs
when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/10.1016/j.cma.2022.115346