Multi-objective Bayesian materials discovery: Application on the discovery of precipitation strengthened NiTi shape memory alloys through micromechanical modeling by Alexandros Solomou, Guang Zhao, Shahin Boluki, Jobin K. Joy, Xiaoning Qian, Ibrahim Karaman, Raymundo Arróyave, Dimitris C. Lagoudas

“…The developed scheme features a Bayesian optimal experimental design process that operates in a closed loop. …”
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Inference and Learning for Active Sensing‚ Experimental Design and Control by Kueck, H, Hoffman, M, Doucet, A, Freitas, N

“…We demonstrate this approach on three examples: (i) active sensing with nonlinear, non-Gaussian, continuous models, (ii) optimal experimental design to discriminate among competing scientific models, and (iii) nonlinear optimal control.…”

A Deep Reinforcement Learning Approach for Active SLAM by Julio A. Placed, José A. Castellanos

“…In this paper, we formulate the active SLAM paradigm in terms of model-free Deep Reinforcement Learning, embedding the traditional utility functions based on the Theory of Optimal Experimental Design in rewards, and therefore relaxing the intensive computations of classical approaches. …”
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Adaptive treatment assignment in experiments for policy choice by Kasy, M, Sautmann, A

“…We consider the problem of treatment assignment in an experiment with several non-overlapping waves, where the goal is to choose among a set of possible policies (treatments) for large-scale implementation. The optimal experimental design learns from earlier waves and assigns more experimental units to the better-performing treatments in later waves. …”

Optimal Designs for Direct Effects: The Case of Two Treatments and Five Periods by Miltiadis S. Chalikias

“…A common problem is to find the distribution of n experimental units in order to find the optimal experimental design for the well-known criteria of optimality (A, D, E optimality, etc.). …”
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Inferring What to Do (And What Not to) by Thomas Parr

“…These are rooted in optimal experimental design, information theory, and statistical decision making. …”
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Minimally sufficient experimental design using identifiability analysis by Jana L. Gevertz, Irina Kareva

“…The framework we propose identifies an optimal experimental design (how much data to collect and when to collect it) that ensures parameter identifiability (permitting confidence in model predictions), while minimizing experimental time and costs. …”
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Active Data Selection and Information Seeking by Thomas Parr, Karl Friston, Peter Zeidman

“…This paper aims to unpack the principles of active sampling of data by drawing from neurobiological research on animal exploration and from the theory of optimal experimental design. We offer an overview of the salient points from these fields and illustrate their application in simple toy examples, ranging from function approximation with basis sets to inference about processes that evolve over time. …”
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Iterative design of training data to control intricate enzymatic reaction networks by Bob van Sluijs, Tao Zhou, Britta Helwig, Mathieu G. Baltussen, Frank H. T. Nelissen, Hans A. Heus, Wilhelm T. S. Huck

“…Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. …”
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Experimental design and analysis for high-parameter spatial omics by Baker, Ethan Alexander García

“…Finally, I share an outlook on how, when considered jointly, these contributions represent first steps towards optimal experimental design for spatial omics.…”
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Study of Effects of MoS₂ Nanofluid MQL Parameters on Cutting Forces and Surface Roughness in Hard Turning Using CBN Insert by Ngo Minh Tuan, Tran The Long, Tran Bao Ngoc

“…Box-Behnken optimal experimental design and ANOVA analysis were used to study the influence of the input parameters and determine the optimal values. …”
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Hellinger Information Matrix and Hellinger Priors by Arkady Shemyakin

“…Hellinger information for the vector parameter was applied by Yin et al. to problems of optimal experimental design. A special class of parametric problems was considered, requiring the directional definition of Hellinger information, but not a full construction of Hellinger information matrix. …”
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Hydrodynamic Interactions of an Unmanned Underwater Vehicle Operating in Close Proximity to a Moving Submarine by Hammond, Brady M.(Brady Meikle)

“…A Gaussian process regression model was used to perform an optimal experimental design and map the resulting hydrodynamic interactions based on the UUVs longitudinal position, lateral position, speed, heading angle, UUV diameter, and UUV length. …”
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Optimisation of an effervescent pineapple tablet by Mohd Rosdan, Faridatul Ain, Yusof, Yus Aniza, Chin, Nyuk Ling, Mohd. Amin, Nor Amaiza, Mohd Ghazali, Hasanah

“…This study is mainly undertaken to design and optimize an effervescent tablet formulation of Josapine pineapple by using the D-optimal experimental design methodology. Josapine pineapple powder, citric acid, sodium carbonate and stevia were used in the formulations as independent variables. …”
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Ancient Metagenomic Studies: Considerations for the Wider Scientific Community by Clio Der Sarkissian, Irina M. Velsko, Anna K. Fotakis, Åshild J. Vågene, Alexander Hübner, James A. Fellows Yates

“…Furthermore, researchers often work with finite sample sizes, which impedes optimal experimental design and control of confounding factors, and with ethically sensitive samples necessitating the consideration of additional guidelines. …”
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Acceleration of CRISPR/Cas9-Mediated Editing at Multiple Sites in the <i>Saccharomyces cerevisiae</i> Genome by Alexey D. Karpukhin, Fanis A. Sabirzyanov, Vsevolod A. Serebrianyi

“…This finding increases the flexibility of choosing the optimal experimental design for multiple editing of the genome of <i>S. cerevisiae</i> and can significantly accelerate such experiments.…”
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Sources of variation in Affymetrix microarray experiments by Parrish Rudolph S, Scheirer Katherine E, Barnes Stephen, Chen Lang, Mehta Tapan, Kim Kyoungmi, Zakharkin Stanislav O, Allison David B, Page Grier P

“…Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were: (1) to estimate relative magnitudes of different sources of variation and (2) to evaluate agreement between biological and technical replicates.…”
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A Python Clustering Analysis Protocol of Genes Expression Data Sets by Giuseppe Agapito, Marianna Milano, Mario Cannataro

“…Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. …”
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Post hoc experimental designs improve genetic trial analyses: A case study of cherrybark oak (Quercus pagoda Raf.) genetic evaluation in the western Gulf region, USA. by Chen Ding, Yuhui Weng, Tom D Byram, Benjamin D Bartlett, Earl M Raley

“…Oaks (Quercus spp.) are widespread hardwood trees in the Northern Hemisphere and of high ecological, economic, and social values. Optimal experimental design of genetic trials is essential for accurate estimates of genetic parameters and improving the genetic merit of breeding stock. …”
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Mine Vegetation Identification via Ecological Monitoring and Deep Belief Network by Bin Gong, Cheng Shu, Song Han, Sheng-Gao Cheng

“…Parameters and parameter values were selected for identification by establishing the optimal experimental design. The experimental results were compared with remote sensing images to determine the accuracy of deep learning identification and the effectiveness of the algorithm. …”
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