A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials, developed by various projects of the University of Twente (NL), the Netherlands eScience Center (NL), University of Newcastle (AU), and Hiroshima University (JP).
(t, m, s)-nets generator / Генератор (t, m, s)-сетей
This is the official repository for the ICML 2026 paper "Neural Low-Discrepancy Sequences"
Quasi-random (low discrepancy) sequence generators with randomizations: Lattice and Digital Nets.
Low-discrepancy sequences based on quadratic irrationals
Souce code for my MSc Thesis in Computer Science "Nested Marvellous Sequences"
Quasi-Monte Carlo engine built from scratch in Python: van der Corput/Halton sequences, exact discrepancy measures, and measured convergence-order gains over ordinary Monte Carlo. 56 tests.
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