part 09/09Educational
Fourier Neural Operators (FNOs)
A step by step guide through the architecture of Fourier Neural Operators, their applications, and how they efficiently process global information through the Fourier transform.
Hey there, I'm Daniel Jonas!
Hey there, my name is Daniel,
a CS master's student focused on deep learning for the simulation of physical systems — and a student AI & Data Scientist at Statista+.
About me
Full CV## Education
2024 — today
M.Sc. Computer Science · AI & Data Science
HAW Hamburg
- GPA 1.0
- Neural ODEs/PDEs & operators, transformers, VAEs, diffusion & flow matching
02/2026 — 07/2026
Postgraduate Exchange Semester Scholarship
UNSW Sydney
- Led a five-student NLP research project on LLM context faithfulness — High Distinction
2020 — 2024
B.Sc. Media Computer Science
University of Flensburg
- Programming, databases, artificial neural networks
## Experience
2025 — today
Working Student · AI & Data Science
Statista
- RAG systems, data automation pipelines, scraping & structuring
2021 — 2025
Working Student · Fullstack Development
Jung von Matt TECH, SOFTSTACK, Events United
- 4+ years of fullstack engineering — projects for dfb.de, bmw.com and more
## Honors
Deutschlandstipendium
Scholarship for Gifted and High-Achieving Students
Physics Simulation Series
start at part 01series · 9 parts · 4 chapters
From ODEs to Neural Operators
Every part of this series builds on the one before it, nothing is used before it has been explained.
Differential equations
The wave equation
Numerical stability
Neural operators
the series is ongoing: new parts are added to the end of the path
Latest Posts
All postspart 09/09
part 08/09Educational
The Fourier Transform, explained for Neural Operators
A semi-deep dive into the Fourier transform and its applications in signal processing and neural networks.
part 07/09Educational
Neural Operators and DeepONet
An introduction to neural operators, DeepONet, and how they can be used to learn mappings between function spaces.
part 06/09Educational
The CFL Condition of the Wave Equation
A deep dive into the Von Neumann stability analysis of the wave equation and its connection to the CFL condition.
part 05/09Educational
Von Neumann Stability Analysis
A deep dive into the von Neumann stability analysis, its connection to the CFL condition, and how it determines the stability of numerical schemes.
part 04/09Educational
Neural Networks as Wave Equation Approximators
A deep dive into the wave equation, its reformulation for neural network approximation, and the challenges of learning dynamics with finite speed.
Recent Projects
All projectsDual MIDI Transformer
A MIDI Event based transformer architecture, utilizing two seperate transformers operating on the global MIDI events and local event parameters.
TransformerDecoderMIDIPyTorch
Basic MIDI Transformer
A basic transformer architecture for MIDI generation, utilizing a single decoder to enable faster training and inference speed.
TransformerDecoderMIDIPyTorch
MoodWave
A real-time streaming pipeline correlating German news sentiment (GDELT) with Spotify Top 200 musical features, using Kafka and Spark Streaming for dual-stream aggregation.
KafkaSparkPySparkNext.jsSupabase