Marc J. Schmidt's starred repositories
ml-engineering
Machine Learning Engineering Open Book
awesome-reMarkable
A curated list of projects related to the reMarkable tablet
sqlite-vec
A vector search SQLite extension that runs anywhere!
docker-rollout
🚀 Zero Downtime Deployment for Docker Compose
starcoder2
Home of StarCoder2!
liquid_time_constant_networks
Code Repository for Liquid Time-Constant Networks (LTCs)
ngxtension-platform
Utilities for Angular
eBPF-Guide
eBPF (extended Berkeley Packet Filter) Guide. Learn all about the eBPF Tools and Libraries for Security, Monitoring , and Networking.
metal-benchmarks
Apple GPU microarchitecture
lagrangian_nns
Lagrangian Neural Networks
remarkable_entware
Entware installer modified for reMarkable Tablet
summarization-eval
📝 Reference-Free automatic summarization evaluation with potential hallucination detection
deep_lagrangian_networks
Open-source implementation of Deep Lagrangian Networks (DeLaN)
PINNs-based-MPC
We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool to approximate (partial) differential equations. PINNs are not suited for control tasks in their original form since they are not designed to handle variable control actions or variable initial values. We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. The high-dimensional input space is subsequently reduced via a sampling strategy and a zero-hold assumption. This strategy enables the controller design based on a PINN as an approximation of the underlying system dynamics. The additional benefit is that the sensitivities are easily computed via automatic differentiation, thus leading to efficient gradient-based algorithms. Finally, we present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system, a multi-link manipulator.
deepkit-restate
Build resilient distributed enterprise applications using Deepkit and Restate