Markus Lange-Hegermann | DeepAI

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∙ 12/29/2022

Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients

Partial differential equations (PDEs) are important tools to model physi...

0 Marc Härkönen, et al. ∙

∙ 08/29/2022

"Prompt-Gamma Neutron Activation Analysis (PGNAA)" Metal Spectral Classification using Deep Learning Method

There is a pressing market demand to minimize the test time of Prompt Ga...

0 Ka Yung Cheng, et al. ∙

∙ 08/29/2022

PGNAA Spectral Classification of Metal with Density Estimations

For environmental, sustainable economic and political reasons, recycling...

4 Helmand Shayan, et al. ∙

∙ 08/26/2022

Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations

Data in many applications follows systems of Ordinary Differential Equat...

0 Andreas Besginow, et al. ∙

∙ 05/06/2022

Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design

Development and optimization of biopharmaceutical production processes w...

0 Tanja Hernández Rodríguez, et al. ∙

∙ 05/06/2022

On boundary conditions parametrized by analytic functions

Computer algebra can answer various questions about partial differential...

0 Markus Lange-Hegermann, et al. ∙

∙ 03/24/2021

Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability

Digitalization leads to data transparency for production systems that we...

0 Tom Hammerbacher, et al. ∙

∙ 02/03/2020

Linearly Constrained Gaussian Processes with Boundary Conditions

One goal in Bayesian machine learning is to encode prior knowledge into ...

0 Markus Lange-Hegermann, et al. ∙

∙ 01/28/2018

Algorithmic Linearly Constrained Gaussian Processes

We algorithmically construct multi-output Gaussian process priors which ...

0 Markus Lange-Hegermann, et al. ∙

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