Results 261 to 270 of about 3,213,103 (353)

NEAMS VU Verification Validation Uncertainty Quantification.

, 2009
J.R. Stewart
openalex   +1 more source

SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models

Association for Computational Linguistics 2025, Bhattacharjya, Debarun, Ganesan, Balaji, Glass, Michael, Lee, Junkyu, Marinescu, Radu, Mirylenka, Katsiaryna, Shou, Xiao   +7 more
openalex   +1 more source

Intrinsic Nanopore‐Assisted SnP2S6 Memristors With Ti Ion Dynamics for Compact Logic‐In‐Memory Hardware

Advanced Functional Materials, EarlyView.
Tin hexathiophosphate memristors leverage intrinsic nanopores together with a guided filament formation strategy to regulate titanium ion motion and switching behavior. The devices support reliable nonvolatile memory and reconfigurable logic‐in‐memory, demonstrating 14 Boolean logic functions in a single cell.
Thaw Tint Te Tun, Sanchali Mitra, Hongxi Su, Jiali Huo, Haofei Zheng, Michel Bosman, Yee Sin Ang, Kah‐Wee Ang   +7 more
wiley   +1 more source

Conformal prediction for uncertainty quantification in dynamic biological systems. [PDF]

PLoS Comput Biol
Portela A, Banga JR, Matabuena M.
europepmc   +1 more source

Quantifying and reducing uncertainties in global mercury cycling

, 2011
Asif Qureshi
openalex   +1 more source

Electrocatalytic Reduction of CO2 to Ethylene: Catalyst Design and Synchrotron‐Based Characterizations

Advanced Functional Materials, EarlyView.
This review evaluates strategies for electrochemical CO2 reduction to ethylene, focusing on copper‐based catalyst design and microenvironment modulation to achieve industrial‐grade performance. By leveraging operando synchrotron‐based characterizations, we provide a multiscale understanding of dynamic structural transformations and key reaction ...
Meng Zhang, Zuolong Chen, Yimin A. Wu
wiley   +1 more source

Blood Flow Simulation and Uncertainty Quantification in Extensive Microvascular Networks: Application to Brain Cortical Networks. [PDF]

Microcirculation
Rasmussen PM.
europepmc   +1 more source

Artificial Intelligence as the Next Visionary in Liquid Crystal Research

Advanced Functional Materials, EarlyView.
The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam, Ouassim L'Karkouri, Danqing Liu   +2 more
wiley   +1 more source

Bayesian deep reinforcement learning for uncertainty quantification and adaptive support optimization in deep foundation pit engineering. [PDF]

Sci Rep
Gu W.
europepmc   +1 more source

Spin Defects in Hexagonal Boron Nitride as 2D Strain Sensors

Advanced Functional Materials, EarlyView.
We demonstrate that boron‐vacancy (VB${\rm V}_{\rm B}$) centers in hexagonal boron nitride (hBN) enable quantitative strain sensing with sub‐micrometer resolution. Using this approach under continuously tunable in‐plane stress, we precisely quantify strain‐induced shifts of the E2g${\rm E}_{2{\rm g}}$ Raman mode in multilayer hBN, establishing VB${\rm ...
Zhao Mu, Zhaowei Zhang, Jules Fraunié, Cédric Robert, Grégory Seine, Bernard Gil, Guillaume Cassabois, Vincent Jacques   +7 more
wiley   +1 more source