2 citations · 2 across the 4 of their papers we have counts for
5 papers
Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations
Johannes Maeß, Leon Werner, J. Thorben Frank +5
We introduce implicit machine learning force fields (I-MLFFs), which replace explicit stacks of neural network layers with self-consistent fixed-point equations. In molecular simul…
Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics
Winfried Ripken, Michael Plainer, Gregor Lied +5
Simulating the long-time evolution of Hamiltonian systems is limited by the small timesteps required for stable numerical integration. To overcome this constraint, we introduce a f…
Sampling 3D Molecular Conformers with Diffusion Transformers
J. Thorben Frank, Winfried Ripken, Gregor Lied +3
Diffusion Transformers (DiTs) have demonstrated strong performance in generative modeling, particularly in image synthesis, making them a compelling choice for molecular conformer…
Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models
Khaled Kahouli, Winfried Ripken, Stefan Gugler +3
Diffusion models have been widely adopted as a powerful generative framework across numerous scientific domains, yet their inherently iterative sampling process remains a significa…
Multiscale Neural Operators for Solving Time-Independent PDEs
Winfried Ripken, Lisa Coiffard, Felix Pieper +1
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…