Results 231 to 240 of about 3,533,074 (286)

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
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, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

The Cerebellum Contributes to Prediction Error Coding in Reinforcement Learning in Humans. [PDF]

J Neurosci
Huvermann DM, Berlijn AM, Thieme A, Erdlenbruch F, Groiss SJ, Deistung A, Mittelstaedt M, Wondzinski E, Sievers H, Frank B, Göricke SL, Gliem M, Köhrmann M, Siebler M, Schnitzler A, Bellebaum C, Minnerop M, Timmann D, Peterburs J.   +18 more
europepmc   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

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, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Active Inference in Music Perception: Motor Engagement to Syncopation Modulates Rhythmic Prediction Error. [PDF]

Psychophysiology
Ishida K, Nittono H.
europepmc   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

A two-step formula constant optimization strategy for minimal standard deviation and zero mean prediction error in IOL power calculation. [PDF]

Acta Ophthalmol
Langenbucher A, Szentmáry N, Wendelstein J, Cayless A, Hoffmann P, Gatinel D.   +5 more
europepmc   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Investigating the Influence of Experimentally Induced Secondary Hyperalgesia on Pain Prediction Error Encoding in Healthy Individuals: A Novel Virtual Reality Protocol. [PDF]

Eur J Pain
Palmisani F, Thiyagarajan JSP, Horsey J, Hughes SW, Medina S.   +4 more
europepmc   +1 more source

Reversible data hiding and authentication scheme for encrypted image based on prediction error compression. [PDF]

Sci Rep
Ren F, Zhang Z, Jiang K, Zhang P, Yang T.   +4 more
europepmc   +1 more source