Winfried Ripken
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View article: Nonequilibrium universality of Rydberg-excitation spreading on a dynamic network
Nonequilibrium universality of Rydberg-excitation spreading on a dynamic network Open
Understanding the universal properties of nonequilibrium phase transitions of spreading processes is a challenging problem. This applies in particular to irregular and dynamically varying networks. We here investigate an experimentally acc…
View article: Sampling 3D Molecular Conformers with Diffusion Transformers
Sampling 3D Molecular Conformers with Diffusion Transformers Open
Diffusion Transformers (DiTs) have demonstrated strong performance in generative modeling, particularly in image synthesis, making them a compelling choice for molecular conformer generation. However, applying DiTs to molecules introduces …
View article: Nonequilibrium Universality of Rydberg-Excitation Spreading on a Dynamic Network
Nonequilibrium Universality of Rydberg-Excitation Spreading on a Dynamic Network Open
Understanding the universal properties of non-equilibrium phase transitions of spreading processes is a challenging problem. This applies in particular to irregular and dynamically varying networks. We here investigate an experimentally ac…
View article: Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models
Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models Open
The long sampling time of diffusion models remains a significant bottleneck, which can be mitigated by reducing the number of diffusion time steps. However, the quality of samples with fewer steps is highly dependent on the noise schedule,…
View article: Multiscale Neural Operators for Solving Time-Independent PDEs
Multiscale Neural Operators for Solving Time-Independent PDEs Open
Time-independent Partial Differential Equations (PDEs) on large meshes pose significant challenges for data-driven neural PDE solvers. We introduce a novel graph rewiring technique to tackle some of these challenges, such as aggregating in…
View article: Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation
Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation Open
This paper presents a methodological framework for training, self-optimising, and self-organising surrogate models to approximate and speed up multiobjective optimisation of technical systems based on multiphysics simulations. At the hand …
View article: Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models Open
Generalized Additive Models (GAMs) have recently experienced a resurgence in popularity due to their interpretability, which arises from expressing the target value as a sum of non-linear transformations of the features. Despite the curren…