Nikolaj Takata Mücke

Integrating Score-Based Diffusion Models with Machine Learning-Enhanced Localization for Advanced Data Assimilation in Geological Carbon Storage

Physics-Aware Generative Models for Turbulent Fluid Flows Through Energy-Consistent Stochastic Interpolants

Generative Super-Resolution of Turbulent Flows via Stochastic Interpolants

The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification

AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage

Advancing Data Assimilation and Uncertainty Quantification for CO2 Sequestration through AI-Hybrid Methods

Machine Learning-Based Digital Twin for Water Distribution Network Anomaly Detection and Localization

Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications

A Probabilistic Digital Twin for Leak Localization in Water Distribution Networks Using Generative Deep Learning

Reduced Order Modelling for Dispersive and Nonlinear Water Wave Modelling

Reduced Order Modelling for Wave-Structure Modelling

Reduced order modeling for parameterized time-dependent PDEs using spatially and memory aware deep learning

Reduced Order Modeling for Nonlinear PDE-constrained Optimization using Neural Networks

Postdoc

I am working as a postdoc in the UrbanAIR project. The goal of the project is to develop a framework for urban air quality and heat dynamics forecasting. My role is specifically focused on the development of data assimilation and uncertainty quantification methods using deep learning techniques.

Postdoc

Together with Benjamin Sanderse, I developed generative models for physics applications. The aim was to perform probabilistic forecasting and posterior sampling that adhere to the underlying laws of physics.

AI Lead

I was the lead developer of the Spatialise AI platform, SOCMO. My role included the development of MLOps pipelines. This covered data science aspects such as model training, testing, and hyperparameter tuning. Furthermore, I was responsible for the ML engineering aspects such as model deployment, monitoring, and scaling.

PhD Candidate

My PhD project dealt with deep learning for data assimilation and inverse problems in physics applications. The aim was to perform real-time data assimilation with uncertainty quantification using deep learning techniques. My supervisors were Cornelis Oosterlee and Sander Bohté.

Research Assistant

The research project dealt with low noise supercontinuum sources for ultra-high resolution 800nm optical coherence tomography for glaucoma diagnosis. I was working on GPU acceleration of a C++ implementation of the 4th order Runge-Kutta Interaction Picture method to solve the generalized nonlinear Schrödinger Equation as well as uncertainty quantification of the input sources.

Student Assistant

My work included programming and mathematical modelling of various elements within the wind energy sector. Examples are time series models for weather with the goal of predicting production time of a wind turbine farm and analyzing buckling capacity of soil supported structures using partial differential equations and optimization techniques.

Science Communicator

Experimentarium is a science museum, mostly for children and young adults. My job consisted of developing and performing science shows and experiments in front of large crowds and make complicated phenomena understandable and comprehensible for the layman and school classes.

Turbulence Closure Modeling using Stochastic Interpolants

Diffusion Models for Time Series Denoising

Guidance in Using Robotic-Arm Assisted Surgical System for Knee Arthroplasty

Stock Price Simulation under Jump-Diffusion Dynamics: A WGANs-Based Framework with Anomaly Detection Techniques

Hamiltonian Neural Networks for Fluid Flows

Traditional and ML approaches to generate and understand implied volatility surfaces

Computational Imaging masterclass

Neural Networks in Finance

Scientific Computing for Differential Equations 2 (02687)

Advanced Engineering Mathematics 2 (01025)

Scientific Computing for Differential Equations (02685)

Physics-consistent generative modeling

Co-Organizer: Benjamin Sanderse

Deep Learning-Based Latent-Space Models for Scientific Computing

Co-Organizer: Wouter Edeling

Workshop Digital Twins for Pipe Transport Networks

Co-Organizers: Prerna Pandey, Shashi Jain, Kees W. Oosterlee, Sander M. Bohte

Machine Learning and Stochastic Modelling for Dynamical Systems

Co-Organizer: Wouter Edeling

Workshop on Machine Learning for Physics-Based Modeling

Co-Organizers: Prerna Pandey, Shashi Jain, Kees W. Oosterlee, Sander M. Bohte

AI and IoT for Flow Modeling

Co-Organizers: Shashi Jain, Kees W. Oosterlee, Sander M. Bohte

Mathematical Modeling and Computation

Mathematics and Technology

Technical University of Munich, Germany

Adelaide University, Australia

International Graduate Summer School on “Frontiers of Applied and Computational Mathematics”

Experiencing China

NWO AINed XS Europe Grant

Oracle Research Grant

Teaching Assistant of the Year

Coursera – Deep Learning Specialization