Name: Patrick Zimbrod
Type: User
Company: University of Augsburg
Bio: PhD Student in computational engineering, mainly metal additive manufacturing. Excited about numerics for PDEs and physics-informed ML.
Location: Augsburg, Germany
Blog: pzimbrod.github.io
Patrick Zimbrod's Projects
A collection of my configs for Linux and MacOS
Learning nonlinear operators via DeepONet
Reference Code in Discontinuous Galerkin Formulation for Benchmark Purposes
Software to train neural networks via Koopman operator theory (see Dogra and Redman "Optimizing Neural Networks via Koopman Operator Theory" NeurIPS 2020 for details).
A collection of example wesbites created with MDwiki
Computational geometry and meshing algorithms in Julia
An (early) attempt to model a L-PBF process using the MOOSE Framework with varying numerical schemes. Currently focusing on simultaneous use of CG and DG.
Source code repository for the publication "An Application Driven Method for Assembling Numerical Schemes for the Solution of Complex Multiphysics Problems"
No need to train, he's a smooth operator
LaTeX style file to add a macro for inserting a linked ORCiD logo
Physics-informed neural networks (PINNs) and deep operator networks for complex PDEs that appear in additive manufacturing.
A beautiful, simple, clean, and responsive Jekyll theme for academics
A Free Open Source Standard Notes Extensions Repository Hosted via Github Pages
A VoF-based multiphase solver including heat transfer, marangoni flow and laser interaction in OpenFOAM intended for modelling complex surface-tension driven flows