Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models
Reduced-order modelling and low-dimensional surrogate models generated using machine
learning algorithms have been widely applied in high-dimensional dynamical systems to …
learning algorithms have been widely applied in high-dimensional dynamical systems to …
Machine learning-combined topology optimization for functionary graded composite structure design
This study presents new framework in which the representative volume element (RVE)
method and machine learning (ML) model are used to construct continuous anisotropic …
method and machine learning (ML) model are used to construct continuous anisotropic …
Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling
Carbonate petroleum reservoirs typically have lower permeabilities and recovery factors
than sandstone reservoirs, so the natural fractures they often incorporate have positive …
than sandstone reservoirs, so the natural fractures they often incorporate have positive …
Polynomial regression as an alternative to neural nets
Despite the success of neural networks (NNs), there is still a concern among many over
their" black box" nature. Why do they work? Here we present a simple analytic argument that …
their" black box" nature. Why do they work? Here we present a simple analytic argument that …
[HTML][HTML] Application of artificial intelligence in the materials science, with a special focus on fuel cells and electrolyzers
Artificial Intelligence (AI) has revolutionized technological development globally, delivering
relatively more accurate and reliable solutions to critical challenges across various research …
relatively more accurate and reliable solutions to critical challenges across various research …
Topological design of thermal conductors using functionally graded materials
This study presents a novel method for the structural design of thermal conductors using
functionally graded materials (FGMs). The effective thermal conductivity tensor components …
functionally graded materials (FGMs). The effective thermal conductivity tensor components …
System identification through Lipschitz regularized deep neural networks
In this paper we use neural networks to learn governing equations from data. Specifically we
reconstruct the right-hand side of a system of ODEs x˙(t)= f (t, x (t)) directly from observed …
reconstruct the right-hand side of a system of ODEs x˙(t)= f (t, x (t)) directly from observed …
[HTML][HTML] Super-resolution on unstructured coastal wave computations with graph neural networks and polynomial regressions
Accurate high-resolution wave forecasts are essential for coastal communities, but local and
even coastal coverage is often still missing due to the heavy computational load of modern …
even coastal coverage is often still missing due to the heavy computational load of modern …
Development of a machine vision-based weight prediction system of butterhead lettuce (Lactuca sativa L.) using deep learning models for industrial plant factory
Introduction Indoor agriculture, especially plant factories, becomes essential because of the
advantages of cultivating crops yearly to address global food shortages. Plant factories have …
advantages of cultivating crops yearly to address global food shortages. Plant factories have …
Multi-objective topological design considering functionally graded materials and coated fiber reinforcement
This study presents a multi-objective topology optimization method tailored to structures
fabricated from functionally graded materials (FGMs), coated FGMs, and coated fiber …
fabricated from functionally graded materials (FGMs), coated FGMs, and coated fiber …