[HTML][HTML] Combining multi-fidelity modelling and asynchronous batch Bayesian Optimization
Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical,
sequential setting of Bayesian Optimization does not translate well into laboratory …
sequential setting of Bayesian Optimization does not translate well into laboratory …
Linear model decision trees as surrogates in optimization of engineering applications
Abstract Machine learning models are promising as surrogates in optimization when
replacing difficult to solve equations or black-box type models. This work demonstrates the …
replacing difficult to solve equations or black-box type models. This work demonstrates the …
Computational Reverse Engineering Analysis of the Scattering Experiment Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D)
Small-angle scattering (SAS) is a widely used characterization technique that provides
structural information in soft materials at varying length scales (nanometers to microns). The …
structural information in soft materials at varying length scales (nanometers to microns). The …
SOBER: Highly parallel Bayesian optimization and Bayesian quadrature over discrete and mixed spaces
Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-
efficient methods of performing optimisation and quadrature where expensive-to-evaluate …
efficient methods of performing optimisation and quadrature where expensive-to-evaluate …
Bounce: reliable high-dimensional Bayesian optimization for combinatorial and mixed spaces
L Papenmeier, L Nardi… - Advances in Neural …, 2023 - proceedings.neurips.cc
Impactful applications such as materials discovery, hardware design, neural architecture
search, or portfolio optimization require optimizing high-dimensional black-box functions …
search, or portfolio optimization require optimizing high-dimensional black-box functions …
Domain-agnostic batch Bayesian optimization with diverse constraints via Bayesian quadrature
Real-world optimisation problems often feature complex combinations of (1) diverse
constraints,(2) discrete and mixed spaces, and are (3) highly parallelisable.(4) There are …
constraints,(2) discrete and mixed spaces, and are (3) highly parallelisable.(4) There are …
Bayesian optimization as a flexible and efficient design framework for sustainable process systems
JA Paulson, C Tsay - arXiv preprint arXiv:2401.16373, 2024 - arxiv.org
Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-
evaluate black-box functions, with a broad range of real-world applications in science …
evaluate black-box functions, with a broad range of real-world applications in science …
Offline Multi-Objective Optimization
Offline optimization aims to maximize a black-box objective function with a static dataset and
has wide applications. In addition to the objective function being black-box and expensive to …
has wide applications. In addition to the objective function being black-box and expensive to …
Bayesian optimisation against climate change: Applications and benchmarks
Bayesian optimisation is a powerful method for optimising black-box functions, popular in
settings where the true function is expensive to evaluate and no gradient information is …
settings where the true function is expensive to evaluate and no gradient information is …
[图书][B] Data-Driven Learning and Optimization of Dynamical Systems
G Makrygiorgos - 2023 - search.proquest.com
Dynamical systems analysis and optimization is pivotal for safe, efficient, and resilient
processes that consistently deliver high-quality products. Conventionally, decision-making …
processes that consistently deliver high-quality products. Conventionally, decision-making …