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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..
7 hours ago by @scadsfct
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topic_lifescience
ALGORITHM
FIS_scads
active
catastrophic
flow
forgetting,
gradient
modeling,
multi-objective
multi-scale
networks,
neural
physics-informed
regularization,
training,
turbulence,
topic_lifescienceALGORITHMFIS_scadsactivecatastrophicflowforgetting,gradientmodeling,multi-objectivemulti-scalenetworks,neuralphysics-informedregularization,training,turbulence,
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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).
7 hours ago by @scadsfct
show all tags
topic_lifescience
Adaptive
Artificial
Bayesian
Carlo,
FIS_scads
Hamiltonian
Intelligence,
Monte
Multi-objective
Quantification
Uncertainty
learning,
networks,
neural
physics-informed
training,
weight
topic_lifescienceAdaptiveArtificialBayesianCarlo,FIS_scadsHamiltonianIntelligence,MonteMulti-objectiveQuantificationUncertaintylearning,networks,neuralphysics-informedtraining,weight
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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.
7 hours ago by @scadsfct
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topic_engineering
Algorithms,
Autonomous
COLREG,
Computer,
Deep
FIS_scads
Networks,
Neural
Psychology,
Recurrency,
Reinforcement,
Reward
Ships,
learning,
reinforcement
surface
vehicle,
topic_engineeringAlgorithms,AutonomousCOLREG,Computer,DeepFIS_scadsNetworks,NeuralPsychology,Recurrency,Reinforcement,RewardShips,learning,reinforcementsurfacevehicle,
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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).
7 hours ago by @scadsfct
show all tags
topic_engineering
area_architectures
Constitutive
FIS_scads
Fiber
Machine
Neural
Parameter
identification
learning,
modeling,
networks,
plastics,
reinforced
topic_engineeringarea_architecturesConstitutiveFIS_scadsFiberMachineNeuralParameteridentificationlearning,modeling,networks,plastics,reinforced
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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.
7 hours ago by @scadsfct
show all tags
topic_engineering
area_architectures
Convolutional
Direct
FIS_scads
Fiber
Machine
Strain
dependency,
identification,
learning,
networks,
neural
parameter
plastics
rate
reinforced
topic_engineeringarea_architecturesConvolutionalDirectFIS_scadsFiberMachineStraindependency,identification,learning,networks,neuralparameterplasticsratereinforced
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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.
7 hours ago by @scadsfct
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Differential
ENO-WENO,
FIS_scads
Neural
Surrogate
modeling
networks,
operators,
DifferentialENO-WENO,FIS_scadsNeuralSurrogatemodelingnetworks,operators,
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1Inverse Dirichlet weighting enables reliable training of physics informed neural networks

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

1Spatial–temporal recurrent reinforcement learning for autonomous ships

1A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP

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

1Learning computable models from data

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