Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …
to wonder what lessons can be learned from other fields undergoing similar developments …
Design of Experiments and machine learning for product innovation: A systematic literature review
The recent increase in digitalization of industrial systems has resulted in a boost in data
availability in the industrial environment. This has favored the adoption of machine learning …
availability in the industrial environment. This has favored the adoption of machine learning …
The effect of pH on stability and thermal performance of graphene oxide and copper oxide hybrid nanofluids for heat transfer applications: application of novel …
This paper investigates the effects of pH on stability and thermal properties of copper oxide
(CuO), graphene oxide (GO), and their hybrid nanofluid (HNF) at different mixing ratios …
(CuO), graphene oxide (GO), and their hybrid nanofluid (HNF) at different mixing ratios …
Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences
The dominant paradigm of experiments in the social and behavioral sciences views an
experiment as a test of a theory, where the theory is assumed to generalize beyond the …
experiment as a test of a theory, where the theory is assumed to generalize beyond the …
Using Bayesian optimization and ensemble boosted regression trees for optimizing thermal performance of solar flat plate collector under thermosyphon condition …
The thermal performance of a flat plate solar collector operating under thermosyphon
conditions using MWCNT+ Fe 3 O 4/Water hybrid nanofluids was investigated in this study …
conditions using MWCNT+ Fe 3 O 4/Water hybrid nanofluids was investigated in this study …
Human–machine collaboration for improving semiconductor process development
One of the bottlenecks to building semiconductor chips is the increasing cost required to
develop chemical plasma processes that form the transistors and memory storage cells …
develop chemical plasma processes that form the transistors and memory storage cells …
Coupling machine learning with 3D bioprinting to fast track optimisation of extrusion printing
Abstract 3D bioprinting, a paradigm shift in tissue engineering holds a promising perspective
for regenerative medicine and disease modelling. 3D scaffolds are fabricated for …
for regenerative medicine and disease modelling. 3D scaffolds are fabricated for …
Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China
Landslide hazard assessment is critical for preventing and mitigating landslide disasters.
The tuning of hyperparameters is of great importance to achieve better accuracy in a …
The tuning of hyperparameters is of great importance to achieve better accuracy in a …
Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning
JH Dunlap, JG Ethier, AA Putnam-Neeb, S Iyer… - Chemical …, 2023 - pubs.rsc.org
We report a human-in-the-loop implementation of the multi-objective experimental design
via a Bayesian optimization platform (EDBO+) towards the optimization of butylpyridinium …
via a Bayesian optimization platform (EDBO+) towards the optimization of butylpyridinium …
Optimizing sequential experimental design with deep reinforcement learning
Bayesian approaches developed to solve the optimal design of sequential experiments are
mathematically elegant but computationally challenging. Recently, techniques using …
mathematically elegant but computationally challenging. Recently, techniques using …