paraklas

University of PennsylvaniaPhiladelphia, PA

Pinned Repositories

DarcyNets
Language:Python9 3 21
deeponet
Learning nonlinear operators via DeepONet
Language:Python2 0 01
GPTutorial
A hands-on tutorial on supervised learning with Gaussian processes
Language:Jupyter Notebook36 2 027
jax-fenics
Differentiable interface to FEniCS for JAX
Language:Python2 0 00
jax-fenics-adjoint
Differentiable interface to FEniCS for JAX using dolfin-adjoint/pyadjoint
Language:Jupyter Notebook1 0 00
Multi-fidelity_GP_DE
Language:MATLAB4 1 04
NARGP
Multi-fidelity modeling using Gaussian processes and nonlinear auto-regressive schemes.
Language:Python58 5 212
PINNs
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
Language:Python5 1 03
sympy2jax
Turning SymPy expressions into JAX functions
Language:Python0 0 00
vdm
Language:Jupyter Notebook0 0 00

paraklas's Repositories

paraklas/NARGP
Multi-fidelity modeling using Gaussian processes and nonlinear auto-regressive schemes.
Language:Python58 5 212
paraklas/GPTutorial
A hands-on tutorial on supervised learning with Gaussian processes
Language:Jupyter Notebook36 2 027
paraklas/DarcyNets
Language:Python9 3 21
paraklas/PINNs
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
Language:Python5 1 03
paraklas/Multi-fidelity_GP_DE
Language:MATLAB4 1 04
paraklas/deeponet
Learning nonlinear operators via DeepONet
Language:Python2 0 01
paraklas/jax-fenics
Differentiable interface to FEniCS for JAX
Language:Python2 0 00
paraklas/jax-fenics-adjoint
Differentiable interface to FEniCS for JAX using dolfin-adjoint/pyadjoint
Language:Jupyter Notebook1 0 00
paraklas/sympy2jax
Turning SymPy expressions into JAX functions
Language:Python0 0 00
paraklas/vdm
Language:Jupyter Notebook0 0 00