Professorship of Data-driven Materials Modeling
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Semi-supervised_Invertible_Neural_Operators
Semi-supervised_Invertible_Neural_Operators PublicSemi-supervised Invertible Neural Operators for Bayesian Inverse Problems
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generative-physics-informed-pde
generative-physics-informed-pde PublicGenerative model employing semi-supervised learning and physical constraints for learning a PDE surrogate.
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Physics-enhanced_NN_SmallData
Physics-enhanced_NN_SmallData PublicPhysics-enhanced Neural Networks in the Small Data Regime
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IncorporatingPhysicalConstraints
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Probabilistic_Koopman_Learning
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predictive-cvs
predictive-cvs PublicPredictive collective variable discovery with deep Bayesian models for atomistic systems.
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- Molecular_Generative_Modeling Public
The goal of inverse molecular design is the discovery of novel molecular structures that fulfill a set of target properties. In this project we explore the application of generative models and molecular graph representations for the development of solutions to the inverse molecular design problem.
pkmtum/Molecular_Generative_Modeling’s past year of commit activity - Semi-supervised_Invertible_Neural_Operators Public
Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems
pkmtum/Semi-supervised_Invertible_Neural_Operators’s past year of commit activity - Embedded_Physics_MD Public
Embedded-physics machine learning for coarse-graining and collective variable discovery without data
pkmtum/Embedded_Physics_MD’s past year of commit activity - cnn-microstructures Public
Code of Bachelor Thesis: Creating CNNs and evaluating predictions of microstructure properties for different parameters
pkmtum/cnn-microstructures’s past year of commit activity - PDE-coarse-grain Public
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime
pkmtum/PDE-coarse-grain’s past year of commit activity
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