jhbastek

PhD student @ ETH Zürich

Zurich, Switzerland

Pinned Repositories

Diffusion-based-Fluid-Super-resolution
PyTorch implementation of the diffusion-based method for CFD data super-resolution proposed in the paper "A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction".
Language:Python117 6 722
fenics-topopt
Topology optimization with finite element analysis in FEniCS. Final project for CSCI-GA.2420: Numerical Methods II at New York University.
Language:Python0 0 01
InvertibleTrussDesign
Code for 'Inverting the structure-property map of truss metamaterials via deep learning'
Language:Python20 1 010
PhysicsInformedDiffusionModels
Implementation of Physics-Informed Diffusion Models
Language:Python25 2 20
PhysicsInformedShellStructures
Code for 'Physics-Informed Neural Networks for Shell Structures'
Language:Python25 2 011
VideoMetamaterials
Implementation of 'Inverse-design of nonlinear mechanical metamaterials via video denoising diffusion models'
Language:Python39 5 48

jhbastek/VideoMetamaterials
Implementation of 'Inverse-design of nonlinear mechanical metamaterials via video denoising diffusion models'
Language:Python39 5 48
jhbastek/PhysicsInformedDiffusionModels
Implementation of Physics-Informed Diffusion Models
Language:Python25 2 20
jhbastek/PhysicsInformedShellStructures
Code for 'Physics-Informed Neural Networks for Shell Structures'
Language:Python25 2 011
jhbastek/InvertibleTrussDesign
Code for 'Inverting the structure-property map of truss metamaterials via deep learning'
Language:Python20 1 010
jhbastek/fenics-topopt
Topology optimization with finite element analysis in FEniCS. Final project for CSCI-GA.2420: Numerical Methods II at New York University.
Language:Python0 0 01