Results 71 to 80 of about 368,237 (276)

Robust Moiety Model Selection Using Mass Spectrometry Measured Isotopologues

Metabolites, 2020
Stable isotope resolved metabolomics (SIRM) experiments use stable isotope tracers to provide superior metabolomics datasets for metabolic flux analysis and metabolic modeling.
Huan Jin, Hunter N.B. Moseley
doaj   +1 more source

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

A comparison of deep convolutional neural networks for image-based detection of concrete surface cracks

Computer Assisted Methods in Engineering and Science, 2019
The aim of this paper is to compare the performance of four deep convolutional neural networks in the problem of image-based automated detection of concrete surface cracks in the case of a small dataset.
Marek Słoński
doaj   +1 more source

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source

A Short Introduction to Model Selection, Kolmogorov Complexity and Minimum Description Length (MDL)

, 2010
The concept of overfitting in model selection is explained and demonstrated with an example. After providing some background information on information theory and Kolmogorov complexity, we provide a short explanation of Minimum Description Length and ...
Nannen, Volker
core   +1 more source

Hyperintensionality and overfitting

Synthese
AbstractA hyperintensional epistemic logic would take the contents which can be known or believed as more fine-grained than sets of possible worlds. I consider one objection to the idea: Williamson’s Objection from Overfitting. I propose a hyperintensional account of propositions as sets of worlds enriched with topics: what those propositions, and so ...
openaire   +3 more sources

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +1 more source

An Optimal Sample Data Usage Strategy to Minimize Overfitting and Underfitting Effects in Regression Tree Models Based on Remotely-Sensed Data

Remote Sensing, 2016
Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model.
Yingxin Gu, Bruce K. Wylie, Stephen P. Boyte, Joshua Picotte, Daniel M. Howard, Kelcy Smith, Kurtis J. Nelson   +6 more
doaj   +1 more source

Uncovering the Origin of Efficiency Roll‐Off in TADF OLEDs

Advanced Functional Materials, EarlyView.
OLEDs based on thermally activated delayed fluorescent (TADF) materials often suffer from a severe drop in efficiency at high brightness levels. This work presents a technique to uncover the source of this efficiency drop, and quantifies the exciton‐exciton and exciton‐polaron annihilation processes responsible for efficiency losses in our TADF OLEDs ...
Liam G. King, Kou Yoshida, Arvydas Ruseckas, Ifor D. W. Samuel   +3 more
wiley   +1 more source

Active Learning‐Accelerated Discovery of Fibrous Hydrogels with Tissue‐Mimetic Viscoelasticity

Advanced Functional Materials, EarlyView.
Active learning accelerates the design of fibrous hydrogels that mimic the viscoelasticity of native tissues. By integrating multi‐objective optimization and closed‐loop experimentation, this approach efficiently identifies optimal formulations from thousands of possibilities and decouples elasticity and viscosity. The resulting hydrogels offer tunable
Zhengkun Chen, Zheyuan Miao, Gary Tom, Austin H. Cheng, Elizaveta Gusarova, Benjamen Zondag, Tianyi Wu, Dianoosh Kalhori, Boris Hinz, Milad Abolhasani, Alán Aspuru‐Guzik, Eugenia Kumacheva   +11 more
wiley   +1 more source