Results 181 to 190 of about 4,878,308 (296)
dynoGP: Deep Gaussian Processes for Dynamic System Identification
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
Ordered Search in Differentiated Markets [PDF]
This paper presents an ordered search model in which consumers search both for price and product fitness. We show that there is price dispersion in equilibrium and prices rise in the order of search.
Zhou, Jidong
core
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Random House Websters College Dictionary
Random House Reference, Random House
core +1 more source
Learning from pandemics: Feature selection and clustering for robust digital disease nowcasting. [PDF]
Mesquita S +4 more
europepmc +1 more source
This study explores the discrepancies between bulk mechanics and spatial mapping in a tissue‐mimetic hydrogel model. Microplastic particles create localized stiff mechanical microenvironments that are detectable by cellular‐scale nanoindentation but leave bulk properties unchanged as measured by rheology.
Ahron T. Verschleisser +3 more
wiley +1 more source
Nature-inspired multi-objective artificial intelligence for short-horizon volatility-regime early warning and defensive asset allocation. [PDF]
Boukeffa H, Djeffal S, Ghoul A.
europepmc +1 more source
Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy
Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang +6 more
wiley +1 more source

