7 papers
EC-EarthFlow: Probabilistic emulation of daily transient global climate model simulations with flow matching
Kirien Whan, Nikolaj T. Mücke, Karin van der Wiel
We introduce EC-EarthFlow, a generative flow matching model that emulates simulations from the physical climate model EC-Earth3. The model is trained on transient simulations from…
Xaurora: Generative Weather Forecasting with Denoising Stochastic Interpolants from a Foundation Model Prior
Eliot Walt, Miltiadis Kofinas, Nikolaj Mücke +2
Deep learning has revolutionised weather forecasting in recent years, especially through atmospheric foundation models, which offer competitive skill for a fraction of the computat…
A Unified Framework for Bayesian Data Assimilation with Generative Models and Observation Interpolants
Nikolaj T. Mücke, Benjamin Sanderse
Bayesian data assimilation combines model forecasts with noisy observations, but sampling high-dimensional, non-Gaussian posteriors remains challenging. We introduce an observation…
Integrating Score-Based Diffusion Models with Machine Learning-Enhanced Localization for Advanced Data Assimilation in Geological Carbon Storage
Gabriel Serrão Seabra, Nikolaj T. Mücke, Vinicius Luiz Santos Silva +3
Accurate characterization of subsurface heterogeneity is important for the safe and effective implementation of geological carbon storage (GCS) projects. This paper explores how ma…
The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification
Nikolaj T. Mücke, Sander M. Bohté, Cornelis W. Oosterlee
In Data Assimilation, observations are fused with simulations to obtain an accurate estimate of the state and parameters for a given physical system. Combining data with a model, h…
Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications
Nikolaj T. Mücke, Benjamin Sanderse, Sander Bohté +1
In the context of solving inverse problems for physics applications within a Bayesian framework, we present a new approach, Markov Chain Generative Adversarial Neural Networks (MCG…
Reduced Order Modeling for Nonlinear PDE-constrained Optimization using Neural Networks
Nikolaj Takata Mücke, Lasse Hjuler Christiansen, Allan Peter Karup-Engsig +1
Nonlinear model predictive control (NMPC) often requires real-time solution to optimization problems. However, in cases where the mathematical model is of high dimension in the sol…