📈 Adaptive: parallel active learning of mathematical functions

CLI tool for flexible and fast adaptive sampling on ONT sequencers

A Python library for Robotic Information Gathering

DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations

Gradient-based adaptive sampling algorithms for self-supervising PINNs

Adaptive Informative Sampling with Environment Partitioning for Heterogeneous Multi-Robot Systems (IROS 2020)

Open-source constructor of surrogates and metamodels

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation, SIGIR 2021

Instructions and scripts for adaptive sampling for gas-surface dynamics

This is a repository associated with the paper "An adaptive sampling and domain learning strategy for multivariate function approximation on unknown domains" by Ben Adcock, Juan M. Cardenas, and Nick Dexter available at https://arxiv.org/abs/2202.00144

Repository for our work "Iterative and Adaptive Sampling Methods in Semiconductor Testing"

Python simulation project for applied mathematics course at Ecole polytechnique

Python implementation of Markov state model-based adaptive sampling guided by SAXS and hybrid information.

Adaptive sampling methods for optimising simulations

Object-Orientated Derivative-Free Optimisation

Implementation and evaluation of the Adaptive Read-time tolerance controller ARTC, used in the evaluation of the paper: "Automatic Tuning of Read-Time Tolerances for Optimized On-Demand Data-Streaming from Sensor Nodes".

This is a repository associated with the paper "CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning" by Ben Adcock, Juan M. Cardenas, and Nick Dexter submitted at Sampling Theory and Applications (SampTa), also available at https://arxiv.org/abs/2208.12190

This is a repository associated with the paper "Near-optimal sampling strategies for multivariate function on general domains" by Ben Adcock and Juan M. Cardenas available at https://epubs.siam.org/doi/10.1137/19M1279459 and https://arxiv.org/abs/1908.01249