Consistent Projection of Langevin Dynamics: Preserving Thermodynamics and Kinetics in Coarse-Grained Models
- URL: http://arxiv.org/abs/2512.03706v1
- Date: Wed, 03 Dec 2025 11:57:29 GMT
- Title: Consistent Projection of Langevin Dynamics: Preserving Thermodynamics and Kinetics in Coarse-Grained Models
- Authors: Vahid Nateghi, Lara Neureither, Selma Moqvist, Carsten Hartmann, Simon Olsson, Feliks Nüske,
- Abstract summary: This work presents a projection-based coarse-graining formalism for general underdamped Langevin dynamics.<n>In addition, we show how the generator Extended Dynamic Mode Decomposition (gEDMD) can be used to model the CG dynamics.
- Score: 1.590657228638911
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Coarse graining (CG) is an important task for efficient modeling and simulation of complex multi-scale systems, such as the conformational dynamics of biomolecules. This work presents a projection-based coarse-graining formalism for general underdamped Langevin dynamics. Following the Zwanzig projection approach, we derive a closed-form expression for the coarse grained dynamics. In addition, we show how the generator Extended Dynamic Mode Decomposition (gEDMD) method, which was developed in the context of Koopman operator methods, can be used to model the CG dynamics and evaluate its kinetic properties, such as transition timescales. Finally, we combine our approach with thermodynamic interpolation (TI), a generative approach to transform samples between thermodynamic conditions, to extend the scope of the approach across thermodynamic states without repeated numerical simulations. Using a two-dimensional model system, we demonstrate that the proposed method allows to accurately capture the thermodynamic and kinetic properties of the full-space model.
Related papers
- Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison [50.0791489606211]
We review state-of-the-art nonlinear model order reduction methods.<n>We discuss both general-purpose methods and tailored approaches for (chemical) process systems.
arXiv Detail & Related papers (2025-06-15T11:39:12Z) - Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations [26.873329262722024]
We present Sequential DIffusion in Functional Tucker space, a novel framework that generates full-field evolution of physical dynamics from irregular sparse observations.<n>We demonstrate significant improvements in both reconstruction accuracy and computational efficiency compared to state-of-the-art approaches.
arXiv Detail & Related papers (2025-05-14T11:09:15Z) - Stochastic generative methods for stable and accurate closure modeling of chaotic dynamical systems [0.0]
deterministic subgrid-scale (SGS) models are often dissipative and unstable, especially in regions of chaotic and turbulent flow.<n>We propose parametric and generative approaches for closure modeling using differential equations (SDEs)<n>We derive and implement a model based on the fluctuations, demonstrating increased accuracy from bridging theoretical models with generative approaches.
arXiv Detail & Related papers (2025-04-13T22:59:42Z) - Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulation [31.868248649812088]
Cloth manipulation is challenging due to its highly complex dynamics, near-infinite degrees of freedom, and frequent self-occlusions.<n>We propose a diffusion-based generative approach for both perception and dynamics modeling.<n>We show that our framework enables effective cloth folding on real robotic systems.
arXiv Detail & Related papers (2025-03-15T05:34:26Z) - Geometric Trajectory Diffusion Models [58.853975433383326]
Generative models have shown great promise in generating 3D geometric systems.
Existing approaches only operate on static structures, neglecting the fact that physical systems are always dynamic in nature.
We propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories.
arXiv Detail & Related papers (2024-10-16T20:36:41Z) - Latent Space Energy-based Neural ODEs [73.01344439786524]
This paper introduces novel deep dynamical models designed to represent continuous-time sequences.<n>We train the model using maximum likelihood estimation with Markov chain Monte Carlo.<n> Experimental results on oscillating systems, videos and real-world state sequences (MuJoCo) demonstrate that our model with the learnable energy-based prior outperforms existing counterparts.
arXiv Detail & Related papers (2024-09-05T18:14:22Z) - Deep generative modelling of canonical ensemble with differentiable thermal properties [0.9421843976231371]
We propose a variational modelling method with differentiable temperature for canonical ensembles.
Using a deep generative model, the free energy is estimated and minimized simultaneously in a continuous temperature range.
The training process requires no dataset, and works with arbitrary explicit density generative models.
arXiv Detail & Related papers (2024-04-29T03:41:49Z) - Dynamic Kernel-Based Adaptive Spatial Aggregation for Learned Image
Compression [63.56922682378755]
We focus on extending spatial aggregation capability and propose a dynamic kernel-based transform coding.
The proposed adaptive aggregation generates kernel offsets to capture valid information in the content-conditioned range to help transform.
Experimental results demonstrate that our method achieves superior rate-distortion performance on three benchmarks compared to the state-of-the-art learning-based methods.
arXiv Detail & Related papers (2023-08-17T01:34:51Z) - Effective Hamiltonian approach to the exact dynamics of open system by complex discretization approximation for environment [0.0]
We propose a generalization of the discretization approximation method into the complex frequency space basing on complex Gauss quadratures.<n>An effective Hamiltonian can be established by this way, which is non-Hermitian and demonstrates the complex energy modes with negative imaginary part.
arXiv Detail & Related papers (2023-03-12T05:34:29Z) - Conditional Generative Models for Simulation of EMG During Naturalistic
Movements [45.698312905115955]
We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms.
We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
arXiv Detail & Related papers (2022-11-03T14:49:02Z) - Photoinduced prethermal order parameter dynamics in the two-dimensional
large-$N$ Hubbard-Heisenberg model [77.34726150561087]
We study the microscopic dynamics of competing ordered phases in a two-dimensional correlated electron model.
We simulate the light-induced transition between two competing phases.
arXiv Detail & Related papers (2022-05-13T13:13:31Z) - Learning Neural Generative Dynamics for Molecular Conformation
Generation [89.03173504444415]
We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph.
We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
arXiv Detail & Related papers (2021-02-20T03:17:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.