Challenges and applications of large language models
Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …
Recent advances in Bayesian optimization
Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
Bayesian Self‐Optimization for Telescoped Continuous Flow Synthesis
The optimization of multistep chemical syntheses is critical for the rapid development of new
pharmaceuticals. However, concatenating individually optimized reactions can lead to …
pharmaceuticals. However, concatenating individually optimized reactions can lead to …
Bayesian optimization with adaptive surrogate models for automated experimental design
Bayesian optimization (BO) is an indispensable tool to optimize objective functions that
either do not have known functional forms or are expensive to evaluate. Currently, optimal …
either do not have known functional forms or are expensive to evaluate. Currently, optimal …
Perspective: Machine learning in experimental solid mechanics
Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …
are rapidly proliferating into the discovery process due to significant advances in data …
A survey on scenario-based testing for automated driving systems in high-fidelity simulation
Automated Driving Systems (ADSs) have seen rapid progress in recent years. To ensure the
safety and reliability of these systems, extensive testings are being conducted before their …
safety and reliability of these systems, extensive testings are being conducted before their …
[BOOK][B] Machine learning in materials science
Machine Learning for Materials Science provides the fundamentals and useful insight into
where Machine Learning (ML) will have the greatest impact for the materials science …
where Machine Learning (ML) will have the greatest impact for the materials science …
Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems
Bayesian optimization (BO) based on Gaussian process regression (GPR) is applied to
different CFD (computational fluid dynamics) problems which can be of practical relevance …
different CFD (computational fluid dynamics) problems which can be of practical relevance …
You only design once (YODO): Gaussian Process-Batch Bayesian optimization framework for mixture design of ultra high performance concrete
Ultra-high-performance concrete (UHPC) has superior strength and durability, and hence it
has been primarily favored in a variety of applications in structural engineering. While the …
has been primarily favored in a variety of applications in structural engineering. While the …
Increasing the scope as you learn: Adaptive Bayesian optimization in nested subspaces
Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-
evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful …
evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful …