SPIEDiff: robust learning of long-time macroscopic dynamics from short-time particle simulations with quantified epistemic uncertainty

• URL: http://arxiv.org/abs/2505.13501v1
• Date: Fri, 16 May 2025 02:03:04 GMT
• Title: SPIEDiff: robust learning of long-time macroscopic dynamics from short-time particle simulations with quantified epistemic uncertainty
• Authors: Zequn He, Celia Reina,
• Abstract summary: SPIEDiff is a machine learning framework designed to overcome limitations in the context of purely dissipative systems.<n>It can deliver accurate predictions with quantified uncertainty in minutes, drastically reducing the computational demand.<n>Overall, SPIEDiff offers a robust and trustworthy pathway for the data-driven discovery of thermodynamic models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The data-driven discovery of long-time macroscopic dynamics and thermodynamics of dissipative systems with particle fidelity is hampered by significant obstacles. These include the strong time-scale limitations inherent to particle simulations, the non-uniqueness of the thermodynamic potentials and operators from given macroscopic dynamics, and the need for efficient uncertainty quantification. This paper introduces Statistical-Physics Informed Epistemic Diffusion Models (SPIEDiff), a machine learning framework designed to overcome these limitations in the context of purely dissipative systems by leveraging statistical physics, conditional diffusion models, and epinets. We evaluate the proposed framework on stochastic Arrhenius particle processes and demonstrate that SPIEDiff can accurately uncover both thermodynamics and kinetics, while enabling reliable long-time macroscopic predictions using only short-time particle simulation data. SPIEDiff can deliver accurate predictions with quantified uncertainty in minutes, drastically reducing the computational demand compared to direct particle simulations, which would take days or years in the examples considered. Overall, SPIEDiff offers a robust and trustworthy pathway for the data-driven discovery of thermodynamic models.

Related papers

• Is memory all you need? Data-driven Mori-Zwanzig modeling of Lagrangian particle dynamics in turbulent flows [38.33325744358047]
  We show how one can learn a surrogate dynamical system that is able to evolve a turbulent Lagrangian trajectory in a way that is point-wise accurate for short-time predictions.<n>This opens up a range of new applications, for example, for the control of active Lagrangian agents in turbulence.
  arXiv  Detail & Related papers  (2025-07-21T20:50:55Z)
• Thermal state preparation by repeated interactions at and beyond the Lindblad limit [41.94295877935867]
  We study the nature of thermalization dynamics and the associated preparation (simulation) time under the repeated interaction protocol.<n>We observe a Mpemba-like effect: Starting from a maximally mixed state, thermalization to an intermediate-temperature state takes longer than to a lower-temperature one.
  arXiv  Detail & Related papers  (2025-06-13T18:34:49Z)
• MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation [48.41289705783405]
  We propose a PDE-embedded network with multiscale time stepping (MultiPDENet)<n>In particular, we design a convolutional filter based on the structure of finite difference with a small number of parameters to optimize.<n>A Physics Block with a 4th-order Runge-Kutta integrator at the fine time scale is established that embeds the structure of PDEs to guide the prediction.
  arXiv  Detail & Related papers  (2025-01-27T12:15:51Z)
• Space-local memory in generalized master equations: Reaching the thermodynamic limit for the cost of a small lattice simulation [0.0]
  We introduce a novel approach that exploits finite memory in time textitand space to efficiently predict the many-body dynamics of dissipative lattice problems. We demonstrate the strengths of this method by focusing on nonequilibrium polaron relaxation and transport in the dispersive Holstein model.
  arXiv  Detail & Related papers  (2024-11-13T13:30:03Z)
• Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology [1.6874375111244329]
  This study investigates the decomposition of total uncertainty in the estimation of states and parameters of a differential system simulated with MC X-TFC. MC X-TFC is applied to a six-compartment stiff ODE system, the CVSim-6 model, developed in the context of human physiology.
  arXiv  Detail & Related papers  (2024-08-13T21:10:39Z)
• Diff-PIC: Revolutionizing Particle-In-Cell Nuclear Fusion Simulation with Diffusion Models [38.46100610494588]
  Nuclear fusion, generally seen as an ultimate solution, has been the focus of intensive research for nearly a century. Recent advancements in Inertial Confinement Fusion have drawn significant attention to fusion research. Laser-Plasma Interaction is critical for ensuring fusion stability and efficiency.
  arXiv  Detail & Related papers  (2024-08-03T19:42:31Z)
• A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators [46.348283638884425]
  We propose a two-step unsupervised deep learning framework named as Latent Autoregressive Recurrent Model (CLARM) for learning dynamics of charged particles in accelerators. The CLARM can generate projections at various accelerator sampling modules by capturing and decoding the latent space representation. The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.
  arXiv  Detail & Related papers  (2024-03-19T22:05:17Z)
• PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers [40.097474800631]
  Time-dependent partial differential equations (PDEs) are ubiquitous in science and engineering. Deep neural network based surrogates have gained increased interest.
  arXiv  Detail & Related papers  (2023-08-10T17:53:05Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations [51.68332623405432]
  Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations.
  arXiv  Detail & Related papers  (2022-05-17T13:08:28Z)
• Physics-aware, probabilistic model order reduction with guaranteed stability [0.0]
  We propose a generative framework for learning an effective, lower-dimensional, coarse-grained dynamical model. We demonstrate its efficacy and accuracy in multiscale physical systems of particle dynamics.
  arXiv  Detail & Related papers  (2021-01-14T19:16:51Z)
• Uncertainty estimation for molecular dynamics and sampling [0.0]
  Machine learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations. It is crucial to obtain an estimate of the error that derives from the finite number of reference structures included during the training of the model. We present examples covering different types of structural and thermodynamic properties, and systems as diverse as water and liquid gallium.
  arXiv  Detail & Related papers  (2020-11-10T00:07:50Z)
• Embedded-physics machine learning for coarse-graining and collective variable discovery without data [3.222802562733787]
  We present a novel learning framework that consistently embeds underlying physics. We propose a novel objective based on reverse Kullback-Leibler divergence that fully incorporates the available physics in the form of the atomistic force field. We demonstrate the algorithmic advances in terms of predictive ability and the physical meaning of the revealed CVs for a bimodal potential energy function and the alanine dipeptide.
  arXiv  Detail & Related papers  (2020-02-24T10:28:41Z)

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.