Stars
A playbook for systematically maximizing the performance of deep learning models.
Practical session on implementing probabilistic ODE solvers at the ProbNumSchool 2024
Data Science for Dynamical System Course
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…
DarkGreyBox: An open-source data-driven python building thermal model inspired by Genetic Algorithms and Machine Learning
[ICLR 2024] Neural Spectral Methods: Self-supervised learning in the spectral domain.
18.335 - Introduction to Numerical Methods course
The best repository showing why transformers might not be the answer for time series forecasting and showcasing the best SOTA non transformer models.
Class materials for computational physics course
The simplest, fastest repository for training/finetuning medium-sized GPTs.
🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.
Lightweight, useful implementation of conformal prediction on real data.
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Bayesian Data Analysis demos for Python
Bringing building energy simulation and statistical learning together
Learning Green's functions of partial differential equations with deep learning.
Physics informed neural networks for control-oriented building thermal models
Materials for Bayesian Methods in Machine Learning Course
All the handwritten notes 📝 and source code files 🖥️ used in my YouTube Videos on Machine Learning & Simulation (https://www.youtube.com/channel/UCh0P7KwJhuQ4vrzc3IRuw4Q)
Material for the "Probabilistic Machine Learning" Course at the University of Tübingen, Summer Term 2023
Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)