Results 121 to 130 of about 149,758 (276)

Sparse Bayesian Neural Networks: Bridging Model and Parameter Uncertainty through Scalable Variational Inference

Mathematics
Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques.
Aliaksandr Hubin, Geir Storvik
doaj   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Fuzzy Data Modeling and Parameter Estimation in Two Gamma Populations

IEEE Access
This study addresses the challenge of estimating parameters for two Gamma populations that share a common scale parameter but differ in their shape parameters, within the context of fuzzy data.
Vijay Kumar Lingutla, Nagamani Nadiminti
doaj   +1 more source

PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference

, 2017
Generalized linear models (GLMs) -- such as logistic regression, Poisson regression, and robust regression -- provide interpretable models for diverse data types.
Adams, Ryan P., Broderick, Tamara, Huggins, Jonathan H.   +2 more
core  

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source

Optimal PD Control Using Conditional GAN and Bayesian Inference

IEEE Access
PD control is a widely used model-free method; however, it often falls short of guaranteeing optimal performance. Optimal model-based control, such as the Linear Quadratic Regulator (LQR), can indeed achieve the desired control performance, but only for ...
Ivan Hernandez, Wen Yu, Xiaoou Li
doaj   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Efficient approximate Bayesian inference for quantifying uncertainty in multiscale animal movement models

Ecological Informatics
It is becoming increasingly important for wildlife managers and conservation ecologists to understand which resources are selected or avoided by an animal and how to best predict future spatial distributions of animal populations in the long term ...
Majaliwa M. Masolele, J. Grant C. Hopcraft, Colin J. Torney   +2 more
doaj   +1 more source

Using approximate Bayesian inference for a "steps and turns" continuous-time random walk observed at regular time intervals. [PDF]

PeerJ, 2020
Ruiz-Suarez S, Leos-Barajas V, Alvarez-Castro I, Morales JM.   +3 more
europepmc   +1 more source