#reducedordermodels search results
🚨 Multiphase flow applications of nonintrusive reduced-order models with Gaussian process emulation T. Botsas, I. Pan, @MasonLR_ & @OkMatar → doi.org/10.1017/dce.20… #Autoencoders #GaussianProcess #ReducedOrderModels #ML #MachineLearning #ComputationalFluidDynamics #Polymers
#Autoencoders are great frameworks for learning #ReducedOrderModels (#ROMs) in #manifold #subspaces. We develop #nonlinear, #hierarchical and #orthogonal #modal #decompositions in #TurbulentFlows. Much more #compact than #POD!! sciencedirect.com/science/articl… @S_LeClainche @Hamideivazi
"Reduced Order Modelling of Hydraulic Valves" by Peter Hall. Peter has done an excellent job on this. Check out his thesis if you are interested in #NeuralNetworks-based #ReducedOrderModels for dynamical systems. cronfa.swan.ac.uk/Record/cronfa6…
And the summer continued with fantastic talks and deep insights from Aditya Nair at @unevadareno, @drSidShinde at @Aptiv, and Chengyun Hua at @ORNL on #phonons #machinelearning #reducedordermodels Looking forward to our next webinar series!
In our new preprint, we present a #MachineLearning approach for the computation of slow invariant manifolds of singularly perturbed #dynamicalsystems for the construction of #reducedordermodels. See how here arxiv.org/abs/2309.07946
Check out this demo on how to produce #ReducedOrderModels using ANSYS, Inc. #CFD lnkd.in/ey8icTP
📢1-year (renewable) #postdocposition in the group led by Giovanni Stabile @uniurbit 🇮🇹 [1/2] 🎯Topics: model order reduction for urban flows, scientific machine learning, and modeling of environmental flows. 💸Net salary: ~ €1700/month. #appliedmathematics #reducedordermodels
![ECCOMASofficial's tweet image. 📢1-year (renewable) #postdocposition in the group led by Giovanni Stabile @uniurbit 🇮🇹 [1/2]
🎯Topics: model order reduction for urban flows, scientific machine learning, and modeling of environmental flows.
💸Net salary: ~ €1700/month.
#appliedmathematics #reducedordermodels](https://pbs.twimg.com/media/GDTzbunXUAAXwG7.jpg)
This work uses Active Learning to enhance non-intrusive Model Order Reduction. The proposed framework can serve for different non-intrusive MOR approaches and help them to build accurate ROMs with as little data as possible. #ReducedOrderModels
Active-learning-based nonintrusive model order reduction Qinyu Zhuang, Dirk Hartmann, Hans-J. Bungartz & @JuanMLorenzi @Siemens @TU_Muenchen @SiemensSoftware → doi.org/10.1017/dce.20… #JointSpaceSampling #MachineLearning #ML #ModelOrderReduction #DigitalTwins #Simulations
Now available - the recording of @MichiganAero's Karthik Duraisamy's #DCEwebinar on Data-driven Reduced Order Models for Multi-scale, Multi-physics Systems, youtube.com/watch?v=9yL5fT… #ReducedOrderModels #RocketEngines #Combustion #RocketEngineering #DataCentricEngineering @UM_MICDE
youtube.com
YouTube
Karthik Duraisamy: Data-Centric Engineering Webinar Series
Check out our new paper rdcu.be/dH1m8 @NatureComms on the construction of #reducedordermodels via #machinelearning for identifying #tippingpoints of #complexsystems. Kudos to the two PhD students @___Giaz, @SSMeridionale, @unina and @NicolasEvangelu @JohnsHopkins
Check out our new paper rdcu.be/dH1m8 @NatureComms on the construction of #reducedordermodels via #machinelearning for identifying #tippingpoints of #complexsystems. Kudos to the two PhD students @___Giaz, @SSMeridionale, @unina and @NicolasEvangelu @JohnsHopkins
📢1-year (renewable) #postdocposition in the group led by Giovanni Stabile @uniurbit 🇮🇹 [1/2] 🎯Topics: model order reduction for urban flows, scientific machine learning, and modeling of environmental flows. 💸Net salary: ~ €1700/month. #appliedmathematics #reducedordermodels
In our new preprint, we present a #MachineLearning approach for the computation of slow invariant manifolds of singularly perturbed #dynamicalsystems for the construction of #reducedordermodels. See how here arxiv.org/abs/2309.07946
"Reduced Order Modelling of Hydraulic Valves" by Peter Hall. Peter has done an excellent job on this. Check out his thesis if you are interested in #NeuralNetworks-based #ReducedOrderModels for dynamical systems. cronfa.swan.ac.uk/Record/cronfa6…
This work uses Active Learning to enhance non-intrusive Model Order Reduction. The proposed framework can serve for different non-intrusive MOR approaches and help them to build accurate ROMs with as little data as possible. #ReducedOrderModels
Active-learning-based nonintrusive model order reduction Qinyu Zhuang, Dirk Hartmann, Hans-J. Bungartz & @JuanMLorenzi @Siemens @TU_Muenchen @SiemensSoftware → doi.org/10.1017/dce.20… #JointSpaceSampling #MachineLearning #ML #ModelOrderReduction #DigitalTwins #Simulations
🚨 Multiphase flow applications of nonintrusive reduced-order models with Gaussian process emulation T. Botsas, I. Pan, @MasonLR_ & @OkMatar → doi.org/10.1017/dce.20… #Autoencoders #GaussianProcess #ReducedOrderModels #ML #MachineLearning #ComputationalFluidDynamics #Polymers
#Autoencoders are great frameworks for learning #ReducedOrderModels (#ROMs) in #manifold #subspaces. We develop #nonlinear, #hierarchical and #orthogonal #modal #decompositions in #TurbulentFlows. Much more #compact than #POD!! sciencedirect.com/science/articl… @S_LeClainche @Hamideivazi
Now available - the recording of @MichiganAero's Karthik Duraisamy's #DCEwebinar on Data-driven Reduced Order Models for Multi-scale, Multi-physics Systems, youtube.com/watch?v=9yL5fT… #ReducedOrderModels #RocketEngines #Combustion #RocketEngineering #DataCentricEngineering @UM_MICDE
youtube.com
YouTube
Karthik Duraisamy: Data-Centric Engineering Webinar Series
And the summer continued with fantastic talks and deep insights from Aditya Nair at @unevadareno, @drSidShinde at @Aptiv, and Chengyun Hua at @ORNL on #phonons #machinelearning #reducedordermodels Looking forward to our next webinar series!
Check out this demo on how to produce #ReducedOrderModels using ANSYS, Inc. #CFD lnkd.in/ey8icTP
🚨 Multiphase flow applications of nonintrusive reduced-order models with Gaussian process emulation T. Botsas, I. Pan, @MasonLR_ & @OkMatar → doi.org/10.1017/dce.20… #Autoencoders #GaussianProcess #ReducedOrderModels #ML #MachineLearning #ComputationalFluidDynamics #Polymers
#Autoencoders are great frameworks for learning #ReducedOrderModels (#ROMs) in #manifold #subspaces. We develop #nonlinear, #hierarchical and #orthogonal #modal #decompositions in #TurbulentFlows. Much more #compact than #POD!! sciencedirect.com/science/articl… @S_LeClainche @Hamideivazi
And the summer continued with fantastic talks and deep insights from Aditya Nair at @unevadareno, @drSidShinde at @Aptiv, and Chengyun Hua at @ORNL on #phonons #machinelearning #reducedordermodels Looking forward to our next webinar series!
📢1-year (renewable) #postdocposition in the group led by Giovanni Stabile @uniurbit 🇮🇹 [1/2] 🎯Topics: model order reduction for urban flows, scientific machine learning, and modeling of environmental flows. 💸Net salary: ~ €1700/month. #appliedmathematics #reducedordermodels
Something went wrong.
Something went wrong.
United States Trends
- 1. Caleb Wilson N/A
- 2. Vesia 4,043 posts
- 3. Grammy 456K posts
- 4. Dizzy 11.5K posts
- 5. Darryn Peterson 1,128 posts
- 6. Kansas 22.3K posts
- 7. #FliffCashFriday 2,519 posts
- 8. End of 1 18.5K posts
- 9. Georgetown 2,427 posts
- 10. #drwfirstgoal N/A
- 11. James Watson 13.4K posts
- 12. Silver Slugger 11.9K posts
- 13. End 1 571K posts
- 14. Thank a Republican 1,505 posts
- 15. NBA Cup 5,832 posts
- 16. Capitol Police 16.7K posts
- 17. Collar 14.8K posts
- 18. #cthsfb N/A
- 19. Jaland Lowe N/A
- 20. Myles Rice N/A