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FEniCS shells; Bayesian inversion for hyper-elastic solids using dolfin-adjoint

fenics-shells: a UFL-based library for simulating thin structures

http://hdl.handle.net/10993/21554

A Bayesian inversion approach to recovering material parameters in hyperelastic solids using dolfin-adjoint

http://hdl.handle.net/10993/21555

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  • Hichem Omrani: Predicting air pollution using remote sensing and sensors measurement

Past events

  • Carlotta Montorsi: Tree based algorithms: implementing Conditional Inference Trees and Forest for old age Frailty Index predictions
  • Minh Vu Chau: Data-driven constitutive laws for hyperelasticity modeling in eigenspace using algorithmic differentiation coupling FEniCS-Pytorch model: application to RVEs
  • Zhiqiang Zhong: Reinforcement Learning based Meta-path Design for Heterogeneous Graph Neural Network
  • Saurabh Deshpande & Milad Zeraatpisheh: Practical implementation session on Neural Networks
  • Arif Sinan Uslu: The effect of self-paced neurofeedback on EEG learning: An experimental setup

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Recent posts

  • Hichem Omrani: Predicting air pollution using remote sensing and sensors measurement
  • Carlotta Montorsi: Tree based algorithms: implementing Conditional Inference Trees and Forest for old age Frailty Index predictions
  • Minh Vu Chau: Data-driven constitutive laws for hyperelasticity modeling in eigenspace using algorithmic differentiation coupling FEniCS-Pytorch model: application to RVEs
  • Zhiqiang Zhong: Reinforcement Learning based Meta-path Design for Heterogeneous Graph Neural Network
  • Saurabh Deshpande & Milad Zeraatpisheh: Practical implementation session on Neural Networks

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