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1A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP
J. Gerritzen, A. Hornig, P. Winkler, and M. Gude. Computational Materials Science, (September 2024)Publisher Copyright: © 2024 The Author(s).
2 months ago by @scadsfct
show all tags
FIS_scads
Fiber
modeling,
area_architectures
plastics,
reinforced
Constitutive
Machine
identification
learning,
networks,
Parameter
topic_engineering
Neural
FIS_scadsFibermodeling,area_architecturesplastics,reinforcedConstitutiveMachineidentificationlearning,networks,Parametertopic_engineeringNeural
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1Artificial neural network small-sample-bias-corrections of the AR(1) parameter close to unit root
H. Jiang, O. Okhrin, and M. Rockinger. Statistica Neerlandica, (Jul 31, 2024)Publisher Copyright: © 2024 The Author(s). Statistica Neerlandica published by John Wiley & Sons Ltd on behalf of Netherlands Society for Statistics and Operations Research..
2 months ago by @scadsfct
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FIS_scads
small
network,
neural
bias
topic_engineering
correction,
sample
FIS_scadssmallnetwork,neuralbiastopic_engineeringcorrection,sample
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1Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning
J. Gerritzen, A. Hornig, P. Winkler, and M. Gude. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, 3, page 1252--1259. European Society for Composite Materials (ESCM), (Jul 2, 2024)21st European Conference on Composite Materials, ECCM 21 ; Conference date: 02-07-2024 Through 05-07-2024.
2 months ago by @scadsfct
show all tags
Convolutional
FIS_scads
Fiber
area_architectures
neural
plastics
rate
dependency,
reinforced
Direct
Strain
Machine
learning,
networks,
identification,
parameter
topic_engineering
ConvolutionalFIS_scadsFiberarea_architecturesneuralplasticsratedependency,reinforcedDirectStrainMachinelearning,networks,identification,parametertopic_engineering
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1Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks
N. Kumar, L. Krause, T. Wondrak, S. Eckert, K. Eckert, and S. Gumhold. Sensors, (2024)
3 months ago by @scadsfct
show all tags
Networks
Noisy
Density
Invertible
Magnetic
Reconstruction
Flux
Fraction
Void
topic_visualcomputing
Neural
NetworksNoisyDensityInvertibleMagneticReconstructionFluxFractionVoidtopic_visualcomputingNeural
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1Inadequacy of common stochastic neural networks for reliable clinical decision support
A. Lindenmeyer, M. Blattmann, S. Franke, T. Neumuth, and D. Schneider. (2024)
2 months ago by @scadsfct
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reliable
clinical
decision
neural
Inadequacy
stochastic
support
networks
reliableclinicaldecisionneuralInadequacystochasticsupportnetworks
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1Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems
S. Perez, S. Maddu, {. Sbalzarini, and P. Poncet. Journal of computational physics, (Oct 15, 2023)Publisher Copyright: © 2023 The Author(s).
2 months ago by @scadsfct
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FIS_scads
training,
Uncertainty
Artificial
Bayesian
Quantification
neural
weight
Multi-objective
Monte
physics-informed
Intelligence,
learning,
Carlo,
networks,
Adaptive
topic_lifescience
Hamiltonian
FIS_scadstraining,UncertaintyArtificialBayesianQuantificationneuralweightMulti-objectiveMontephysics-informedIntelligence,learning,Carlo,networks,Adaptivetopic_lifescienceHamiltonian
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1Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach
M. Steinacker, Y. Kheifetz, and M. Scholz. Heliyon, 9 (7): e17890 (July 2023)
5 months ago by @scadsfct
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neural
networks;
identification;
Recurrent
learning
Haematopoiesis;
unit_transfer
System
Precision
topic_mathfoundation
topic_neuroinspired
Transfer
medicine;
neuralnetworks;identification;RecurrentlearningHaematopoiesis;unit_transferSystemPrecisiontopic_mathfoundationtopic_neuroinspiredTransfermedicine;
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1Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network
A. Iporre-Rivas, D. Saur, K. Rohr, G. Scheuermann, and C. Gillmann. J. Med. Imaging (Bellingham), 10 (4): 044502 (July 2023)
5 months ago by @scadsfct
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stroke
medical
learning;
neural
networks;
imaging;
machine
prediction
multi-modal
topic_visualcomputing
graph
strokemedicallearning;neuralnetworks;imaging;machinepredictionmulti-modaltopic_visualcomputinggraph
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1Spatial–temporal recurrent reinforcement learning for autonomous ships
M. Waltz, and O. Okhrin. Neural Networks, 2023 (165): 634--653 (Jun 15, 2023)Publisher Copyright: © 2023 Elsevier Ltd.
2 months ago by @scadsfct
show all tags
FIS_scads
Networks,
Autonomous
surface
Computer,
Recurrency,
Deep
vehicle,
COLREG,
learning,
Algorithms,
Psychology,
Ships,
Reward
reinforcement
topic_engineering
Neural
Reinforcement,
FIS_scadsNetworks,AutonomoussurfaceComputer,Recurrency,Deepvehicle,COLREG,learning,Algorithms,Psychology,Ships,Rewardreinforcementtopic_engineeringNeuralReinforcement,
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2Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulse
M. Bogdan. XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja), Servizo de Publicacións da UDC, (September 2022)
29 days ago by @scadsfct
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Learning
neural
algorithms
spiking
learning
plausible
networks
neurological
Learningneuralalgorithmsspikinglearningplausiblenetworksneurological
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1Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology
N. Ghaffari Laleh, H. Muti, C. Loeffler, A. Echle, O. Saldanha, F. Mahmood, M. Lu, C. Trautwein, R. Langer, B. Dislich and 13 other author(s). Med. Image Anal., 79 (102474): 102474 (July 2022)
5 months ago by @scadsfct
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Convolutional
Artificial
intelligence;
Multiple-Instance
deep
neural
networks;
learning
Weakly-supervised
Learning;
transformers;
pathology;
topic_lifescience
Vision
Computational
ConvolutionalArtificialintelligence;Multiple-Instancedeepneuralnetworks;learningWeakly-supervisedLearning;transformers;pathology;topic_lifescienceVisionComputational
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1Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction
D. Nam, J. Chapiro, V. Paradis, T. Seraphin, and J. Kather. JHEP Rep., 4 (4): 100443 (April 2022)
5 months ago by @scadsfct
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a
disease;
HCC,
or
intelligence;
liver
multivariable
non-alcoholic
multimodal
ML,
artificial
WSIs,
images;
prediction
support
integration
TACE,
DICOM,
AI,
network;
deep
neural
Digital
Diagnosis;
MVI,
transarterial
fatty
topic_lifescience
microvascular
convolutional
in
learning;
imaging;
Communications
invasion;
NAFLD,
chemoembolisation;
hepatocellular
Reporting
steatohepatitis;
Transparent
Individual
slide
machine
Prognosis
Artificial
data
for
NASH,
whole
Medicine;
TRIPOD,
of
CNN,
and
Imaging
carcinoma;
diagnostic
system;
model
adisease;HCC,orintelligence;livermultivariablenon-alcoholicmultimodalML,artificialWSIs,images;predictionsupportintegrationTACE,DICOM,AI,network;deepneuralDigitalDiagnosis;MVI,transarterialfattytopic_lifesciencemicrovascularconvolutionalinlearning;imaging;Communicationsinvasion;NAFLD,chemoembolisation;hepatocellularReportingsteatohepatitis;TransparentIndividualslidemachinePrognosisArtificialdataforNASH,wholeMedicine;TRIPOD,ofCNN,andImagingcarcinoma;diagnosticsystem;model
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1Inverse Dirichlet weighting enables reliable training of physics informed neural networks
S. Maddu, D. Sturm, {. Mueller, and {. Sbalzarini. Machine learning: science and technology, (Feb 15, 2022)Funding Information: This work was supported by the German Research Foundation (DFG) – EXC-2068, Cluster of Excellence ‘Physics of Life’, and by the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, funded by the Federal Ministry of Education and Research (BMBF). The present work was also partly funded by the Center for Advanced Systems Understanding (CASUS), which is financed by Germany’s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament. Publisher Copyright: © 2022 The Author(s). Published by IOP Publishing Ltd..
2 months ago by @scadsfct
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FIS_scads
training,
modeling,
neural
multi-scale
ALGORITHM
active
physics-informed
forgetting,
networks,
regularization,
gradient
topic_lifescience
catastrophic
turbulence,
flow
multi-objective
FIS_scadstraining,modeling,neuralmulti-scaleALGORITHMactivephysics-informedforgetting,networks,regularization,gradienttopic_lifesciencecatastrophicturbulence,flowmulti-objective
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1Introduction to the BioChemical Library (BCL): An application-based open-source toolkit for integrated cheminformatics and machine learning in computer-aided drug discovery
B. Brown, O. Vu, A. Geanes, S. Kothiwale, M. Butkiewicz, E. Lowe, Jr, R. Mueller, R. Pape, J. Mendenhall, and J. Meiler. Front. Pharmacol., (February 2022)
5 months ago by @scadsfct
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discovery;
library;
cheminformatics;
deep
network;
neural
biochemical
open-source
BCL;
drug
design;
topic_lifescience
QSAR;
discovery;library;cheminformatics;deepnetwork;neuralbiochemicalopen-sourceBCL;drugdesign;topic_lifescienceQSAR;
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1Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review
L. Schneider, S. Laiouar-Pedari, S. Kuntz, E. Krieghoff-Henning, A. Hekler, J. Kather, T. Gaiser, S. Fröhling, and T. Brinker. Eur. J. Cancer, (January 2022)
5 months ago by @scadsfct
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Convolutional
Biomarker
Multimodal
neural
networks;
Omics
identification;
topic_lifescience
Cancer;
fusion;
ConvolutionalBiomarkerMultimodalneuralnetworks;Omicsidentification;topic_lifescienceCancer;fusion;
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1SetQuence & SetOmic: Deep Set Transformer-based Representations of Cancer Multi-Omics
N. Jurenaite, D. Leon-Perinan, V. Donath, S. Torge, and R. Jakel. 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022, page 139--147. United States of America, IEEE, New York u. a., (2022)Publisher Copyright: © 2022 IEEE.; 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022 ; Conference date: 15-08-2022 Through 17-08-2022.
2 months ago by @scadsfct
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FIS_scads
Set
processing,
gene
expression,
natural
language
molecular
livinglab
genome,
Deep
sequence
topic_federatedlearn
multi-omics,
mutome,
Representations
analysis,
Network,
Neural
FIS_scadsSetprocessing,geneexpression,naturallanguagemolecularlivinglabgenome,Deepsequencetopic_federatedlearnmulti-omics,mutome,Representationsanalysis,Network,Neural
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1Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs
C. Xu, F. Su, and J. Lehmann. (2022)
29 days ago by @scadsfct
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Entity
Networks
Graph
Alignment
Time-aware
Temporal
Graphs
Yaff
Knowledge
Neural
EntityNetworksGraphAlignmentTime-awareTemporalGraphsYaffKnowledgeNeural
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2Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks
A. Marcolongo, M. Vladymyrov, S. Lienert, N. Peleg, S. Haug, and J. Zscheischler. Environmental Data Science, (2022)
2 months ago by @scadsfct
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Convolutional
Networks
Predicting
low
gross
production
data
primary
Neural
weather
ConvolutionalNetworksPredictinglowgrossproductiondataprimaryNeuralweather
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1OmniOpt – A tool for hyperparameter optimization on HPC
P. Winkler, N. Koch, A. Hornig, and J. Gerritzen. High Performance Computing - ISC High Performance Digital 2021 International Workshops, 2021, Revised Selected Papers, page 285--296. Germany, Springer, Berlin u. a., (Nov 13, 2021)Publisher Copyright: © 2021, Springer Nature Switzerland AG.; International Conference on High Performance Computing, ISC High Performance 2021 ; Conference date: 24-06-2021 Through 02-07-2021.
2 months ago by @scadsfct
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FIS_scads
computing,
High
optimization,
Hyperparameter
performance
networks
Neural
FIS_scadscomputing,Highoptimization,HyperparameterperformancenetworksNeural
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1Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks
V. Scholz, P. Winkler, A. Hornig, M. Gude, and A. Filippatos. Sensors (Basel), 21 (6): 2005 (March 2021)
5 months ago by @scadsfct
show all tags
convolutional
(SHM)
monitoring
learning;
neural
networks;
rotors;
health
dense
composites;
connected
structural
composite
machine
fully
convolutional(SHM)monitoringlearning;neuralnetworks;rotors;healthdensecomposites;connectedstructuralcompositemachinefully
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1Learning computable models from data
S. Maddu, D. Sturm, {. Cheeseman, {. Müller, and {. Sbalzarini. (2021)Publisher Copyright: © 2021, Univelt Inc., All rights reserved.; 14th World Congress of Computational Mechanics and ECCOMAS Congress, WCCM-ECCOMAS 2020 ; Conference date: 11-01-2021 Through 15-01-2021.
2 months ago by @scadsfct
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FIS_scads
modeling
Surrogate
Differential
networks,
ENO-WENO,
Neural
operators,
FIS_scadsmodelingSurrogateDifferentialnetworks,ENO-WENO,Neuraloperators,
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1TraceVis: Towards Visualization for Deep Statistical Model Checking
T. Gros, D. Groß, S. Gumhold, J. Hoffmann, M. Klauck, and M. Steinmetz. Leveraging Applications of Formal Methods, Verification and Validation: Tools and Trends: 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Rhodes, Greece, October 20–30, 2020, Proceedings, Part IV, page 27–46. Berlin, Heidelberg, Springer-Verlag, (2020)
3 months ago by @scadsfct
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Networks
Visualization
Model
Statistical
Checking
Neural
NetworksVisualizationModelStatisticalCheckingNeural
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1A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP

1Artificial neural network small-sample-bias-corrections of the AR(1) parameter close to unit root

1Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning

1Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks

1Inadequacy of common stochastic neural networks for reliable clinical decision support

1Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems

1Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach

1Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network

1Spatial–temporal recurrent reinforcement learning for autonomous ships

2Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulse

1Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology

1Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

1Inverse Dirichlet weighting enables reliable training of physics informed neural networks

1Introduction to the BioChemical Library (BCL): An application-based open-source toolkit for integrated cheminformatics and machine learning in computer-aided drug discovery

1Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review

1SetQuence & SetOmic: Deep Set Transformer-based Representations of Cancer Multi-Omics

1Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs

2Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks

1OmniOpt – A tool for hyperparameter optimization on HPC

1Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks

1Learning computable models from data

1TraceVis: Towards Visualization for Deep Statistical Model Checking

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