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-geostatguy_branches_excel

A set of numerical demonstrations in Excel to assist with teaching / learning concepts in probability, statistics, spatial data analytics and geostatistics. I hope these resources are helpful, Prof. Michael Pyrcz

2d-channel-torch

Channel Flow simulation using Physics informed neural networks

camlab-dlsctutorials

cpinns_on_discrete_domains_for_conservation_laws

cpinns_original

We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation property of cPINN is obtained by enforcing the flux continuity in the strong form along the sub-domain interfaces.

dlsc-2023

ETH Zürich Deep Learning in Scientific Computing Master's course 2023

geostatguy_branches_ml

My graduate level machine learning course, including student machine learning projects.

geostatguy_branches_python

Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.

learning-python-physics-informed-machine-learning-pinns-deeponets

Physics Informed Machine Learning Tutorials (Pytorch and Jax)

nsfnets

PINN in solving Navier–Stokes equation

pinn-ch-allencahn

A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations

pinn_2d_transient

pinn_ldc_steady_tf

pinn_ldc_tf_steady_complex

A Physics Informed Neural Network to Solve a Classical Fluid Flow Problem of a Lid Driven Square Cavity

pinn_pytorch_cylinder_wake_batch

PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations

pinn_torch_improved_wts_nvs

An improved and generic PINNs for fluid dynamic analysis is proposed. This approach incorporates three key improvements: residual-based adaptive sampling, which automatically samples points in areas with larger residuals; adaptive loss weights, which balance the loss terms effectively; and the utilization of the DE optimization algorithm

pinns

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

pinns_cylinder_wake

pytorch-deep-learning

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

xpinns_original

Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations