Results 51 to 60 of about 115,779 (267)

A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function [PDF]

open access: yesNeural Computation, 2016
The possibility of approximating a continuous function on a compact subset of the real line by a feedforward single hidden layer neural network with a sigmoidal activation function has been studied in many papers. Such networks can approximate an arbitrary continuous function provided that an unlimited number of neurons in a hidden layer is permitted ...
Namig J. Guliyev, Vugar E. Ismailov
openaire   +5 more sources

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

open access: yesAdvanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann   +8 more
wiley   +1 more source

Investigation of four-layer multi-layer perceptron with glia connections of hidden-layer neurons [PDF]

open access: yesThe 2013 International Joint Conference on Neural Networks (IJCNN), 2013
A glia is a nervous cell existing in a brain. This cell transmits signals by various ion concentrations. By the ion concentration, the glia correlates the neuron by ions. Thus, the glia composes the network different to the neural network. We proposed a four-layer Multi-Layer Perceptron (MLP) with glia connections of hidden-layer neurons which is ...
Chihiro Ikuta   +2 more
openaire   +2 more sources

PASTA‐ELN: Simplifying Research Data Management for Experimental Materials Science

open access: yesAdvanced Engineering Materials, EarlyView.
Research data management faces ongoing hurdles as many ELNs remain complex and restrictive. PASTA‐ELN offers an open‐source, cross‐platform solution that prioritizes simplicity, offline access, and user control. Its in tuitive folder structure, modular Python add‐ons, and open formats enable seamless documentation, FAIR data practices, and easy ...
S. Brinckmann, G. Winkens, R. Schwaiger
wiley   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

open access: yesAdvanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle   +5 more
wiley   +1 more source

Syllables sound signal classification using multi-layer perceptron in varying number of hidden-layer and hidden-neuron

open access: yesMATEC Web of Conferences, 2018
The research on signal processing of syllables sound signal is still the challenging tasks, due to non-stationary, speaker-dependent, variable context, and dynamic nature factor of the signal. In the process of classification using multi-layer perceptron
Kristomo Domy   +2 more
doaj   +1 more source

Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy

open access: yesAdvanced 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

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

open access: yesAdvanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani   +7 more
wiley   +1 more source

Toward Full Interoperability in Materials Science: Integrating Workflows With Knowledge Graphs

open access: yesAdvanced Engineering Materials, EarlyView.
The connection of conceptual workflow design, portable execution, and ontology‐based semantics leading to provenance‐rich knowledge graphs are main contributors to interoperability in materials science and a prerequisite to AI‐assisted orchestration and for interoperable Materials Acceleration Platforms.
Jan Janssen   +14 more
wiley   +1 more source

Supporting AI Readiness Through Digital Workflows in Materials Science

open access: yesAdvanced Engineering Materials, EarlyView.
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns   +67 more
wiley   +1 more source

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