Advance knowledge at the Interface of AI and computational physics (scientific machine learning, data assimilation, physics-informed AI, uncertainty quantification
CoMSAIL (Jian-Xun Wang Research Group at Cornell)
We try to advance knowledge at the Interface of AI and computational physics (SciML, AI4Science, Differentiable Physics, Data assimilation, UQ)
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FM-SWAG-FlowReconstruct
FM-SWAG-FlowReconstruct PublicConditional flow matching for generative modeling of near-wall turbulence with quantified uncertainty
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PiNDiff-CVI
PiNDiff-CVI PublicProbabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process
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Repositories
Showing 10 of 20 repositories
- FM-SWAG-FlowReconstruct Public
Conditional flow matching for generative modeling of near-wall turbulence with quantified uncertainty
- HybridNDiff-UQ Public
Code repo for HybridNDiff-UQ: Uncertainty Quantification for Hybrid Neural Differentiable Modeling
- .github Public
- confild-inlet Public
- CoNFiLD Public
Code repo for CoNFiLD: Conditional Neural Field Latent Diffusion Model Generating Spatiotemporal Turbulence
- EKI-hybridNeural Public
Iterative Mini-batching Ensemble Kalman Inversion for Non-differentiable Hybrid Neural Model
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