Results 71 to 80 of about 9,437,008 (290)
Advanced Engineering Materials, EarlyView.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
Behaviour analysis for web-mediated active learning [PDF]
, 2006 Software-mediated learning requires adjustments in the teaching and learning process. In particular active learning facilitated through interactive learning software differs from traditional instructor-oriented, classroom-based teaching.
Claus Pahl, Pahl, Claus
core +4 more sources
Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory
Entropy, 2020 Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training ...
Sergey Oladyshkin +3 more
doaj +1 more source
Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying
Advanced Engineering Materials, EarlyView.An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara +8 more
wiley +1 more source
Reinforcement learning or active inference? [PDF]
, 2009 This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception.
Friston, K. +17 more
core +1 more source
Bayesian Active Learning for Structured Output Design
CoRR, 2019 In this paper, we propose an active learning method for an inverse problem that aims to find an input that achieves a desired structured-output. The proposed method provides new acquisition functions for minimizing the error between the desired structured-output and the prediction of a Gaussian process model, by effectively incorporating the ...
Kota Matsui +5 more
openaire +3 more sources
Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy
Advanced Engineering Materials, EarlyView.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
Issues and Requirements for Bayesian Approaches in Context Aware Systems [PDF]
, 2005 Research in advanced context-aware systems has clearly shown a need to capture the inherent uncertainty in the physical world, especially in human behavior.
Strang, Thomas +5 more
core +1 more source
Reflections on Active Learning Approaches [PDF]
, 2022 Student Engagement - Active Learning approaches help me to engage more and understand information better. They help me take control of my learning process.
Bergin, Melissa
core +1 more source