Search Results - "Mücke, Nikolaj T."

The deep latent space particle filter for real-time data assimilation with uncertainty quantification by Mücke, Nikolaj T., Bohté, Sander M., Oosterlee, Cornelis W.

“…In data assimilation, observations are fused with simulations to obtain an accurate estimate of the state and parameters for a given physical system. Combining…”
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A Probabilistic Digital Twin for Leak Localization in Water Distribution Networks Using Generative Deep Learning by Mücke, Nikolaj T, Pandey, Prerna, Jain, Shashi, Bohté, Sander M, Oosterlee, Cornelis W

“…Localizing leakages in large water distribution systems is an important and ever-present problem. Due to the complexity originating from water pipeline…”
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Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications by Mücke, Nikolaj T., Sanderse, Benjamin, Bohté, Sander M., Oosterlee, Cornelis W.

“…In the context of solving inverse problems for physics applications within a Bayesian framework, we present a new approach, the Markov Chain Generative…”
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Reduced order modeling for parameterized time-dependent PDEs using spatially and memory aware deep learning by Mücke, Nikolaj T., Bohté, Sander M., Oosterlee, Cornelis W.

“…•Non-intrusive reduced order model for parameterized dynamic PDEs using deep learning.•Dimensionality reduction using convolutional autoencoders.•Time stepping…”
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AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage by Seabra, Gabriel Serrão, Mücke, Nikolaj T., Silva, Vinicius Luiz Santos, Voskov, Denis, Vossepoel, Femke C.

“…This study investigates the integration of machine learning (ML) and data assimilation (DA) techniques, focusing on implementing surrogate models for…”
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The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification by Mücke, Nikolaj T, Bohté, Sander M, Oosterlee, Cornelis W

“…In Data Assimilation, observations are fused with simulations to obtain an accurate estimate of the state and parameters for a given physical system. Combining…”
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Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications by Mücke, Nikolaj T, Sanderse, Benjamin, Bohté, Sander, Oosterlee, Cornelis W

“…In the context of solving inverse problems for physics applications within a Bayesian framework, we present a new approach, Markov Chain Generative Adversarial…”
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Reduced Order Modeling for Parameterized Time-Dependent PDEs using Spatially and Memory Aware Deep Learning by Mücke, Nikolaj T, Bohté, Sander M, Oosterlee, Cornelis W

“…We present a novel reduced order model (ROM) approach for parameterized time-dependent PDEs based on modern learning. The ROM is suitable for multi-query…”
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