A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods

A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

The implementation of MGNNI: Multiscale Graph Neural Networks with Implicit Layers (NeurIPS 2022)

Code for the DEQ experiments of the ICLR 2022 spotlight "SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models"

Code for the bi-level experiments of the ICLR 2022 paper "SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models" (on branch shine)

Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129

Audiotracks Recommender System, Author: Kirill N.

Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379

Source code for "Efficient Bayesian Experimental Design for Implicit Models", AISTATS 2019, https://arxiv.org/abs/1810.09912

[ECE NTUA] 🎓 Diploma Thesis - Compressed Sensing MRI using Score-based Implicit Model (2022)

Python code for "Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods", NeurIPS, 2021, https://proceedings.neurips.cc/paper/2021/hash/d811406316b669ad3d370d78b51b1d2e-Abstract.html

Code for the paper "Sequential Bayesian Experimental Design for Implicit Models via Mutual Information", Bayesian Analysis 2021, https://arxiv.org/abs/2003.09379.

A Blender add-on with a collection of tools for 3D geological modelling, primarily designed for mineral exploration. It allows the import and visualization of drill holes and related interval data, point data, generation of simple block models, grade shell meshes, and integrates with the GemPy module for implicit geological modelling