Top Tweets for #MultiFidelity
Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials | Journal of the American Chemical Society @SeoulNatlUni #Data #Multifidelity #Trainning #ML #Interatomic #Potentials https://t.co/OqAUW5VyTm
Great new work by Amanda Howard, Yucheng Fu and Panos Stinis @PNNLab - 'A #multifidelity approach to continual learning for #physicalsystems' - https://t.co/KSsZn0EUFq #machinelearning #PINNs #neuralnetworks #datascience #AI #compphys #modelling #energy #HPC #physics
๐ Interested in #emulation with #ML?
๐Check "#Multifidelity #GaussianProcess #Emulation for #Atmospheric RTMs"
https://t.co/ZveAvq2ExY
๐ฐ๏ธ Multifidelity nested GPs
๐ Impact of fidelity layers, dim. red. & data size
๐ Remarkable results in atmospheric correction
Wonderful to collaborate with @Savage_Tom @AntonioE89 @Naush13 @jonRmcdonough ๐
Now on @arxiv https://t.co/pEx82R5OtB
#MachineLearning #simulations #multifidelity
@PREMIERE_UKRI @NewcastlePIG @sargent_centre
In our latest preprint we introduce DARTS๐ฏ, a multi-fidelity framework for the Design and Analysis of Reactor and Tube Simulations.
Multiple continuous fidelity params โ
Sim cost = function of design vars + fidelities โ
3D printed optimal geometryโ
https://t.co/BhwJu7M4T2
Meet Katerina Giannoukou, our ESR13, who is conducting her PhD at ETH Zรผrich, working on multifidelity grey-box modelling!
#research #greyboxmodels #multifidelity #MachineLearning
https://t.co/aw4nFtZ7r4
Check out our latest work showcasing a simple denoising approach to exploit #Multifidelity data for #MachineLearning #Materials properties published in #npjcompumats : https://t.co/1fVh4ZDb29
#MetaLearning how to select models for a given task using #Multifidelity information from #Learningcurves ? Oh, most certainly!
International #Conference on #Uncertainty #Quantification & #Optimisation #UQOP2020
Ben Parsonage talks about "A #multifidelity model management framework for multi-objective #aerospace design optimisation"
@MAE_Strath
Honored to co-chair with Dr. Laura Mainini (UTRC) the first special session on #multifidelity methods for air and sea vehicle applications at #AiaaAviation. Q&A virtual session will take place today 2:00PM-3:00PM EDT at https://t.co/SXyIR6guo5
@InmCnr
Casey's paper on #multifidelity #UQ is out in #CMAME! Nice example of linking #opensource packages @SimVascular and @SandiaLabs Dakota to drive variance reduction in cardiovascular modeling. https://t.co/MWKEgYLzNy
#multifidelity #modeling- low resolution #sensors predict trends, high res sensors to determine values. Together-correct function @shahselbe
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