Climate Physics Dynamic Matching

• URL: http://arxiv.org/abs/2608.26907v1
• Date: Thu, 27 Aug 2026 10:07:40 GMT
• Title: Climate Physics Dynamic Matching
• Abstract summary: We present Climate Physics Dynamic Matching (ClimPhyDM), a variational simulation-free dynamics informed framework for weather forecasting.<n>On the ERA5 benchmark at hourly (42-hour) and monthly (5-month) resolutions, ClimPhyDM outperforms ClimODE, and GB-DM, keeping the lower error at extended horizon.
• Score: 46.03496190842378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep generative models such as flow matching and diffusion models have shown potential for learning complex dynamical systems, but typically act as black boxes that neglect underlying physical structure, while physics-based models governed by partial differential equations are often incomplete due to missing source terms, or uncertain parametrisations. We present Climate Physics Dynamic Matching (ClimPhyDM), a variational simulation-free dynamics informed framework for weather forecasting that combines an advection-type physics prior with data-driven components in a variational framework. % to capture the stochasticity and multi-modality of unresolved atmospheric dynamics. On the ERA5 benchmark at hourly (42-hour) and monthly (5-month) resolutions, ClimPhyDM outperforms ClimODE, and GB-DM, keeping the lower error at extended horizon, indicating improved temporal stability and resistance to error accumulation, while its simulation-free paradigm also enables training on a single modest 12 GB consumer GPU.

Related papers

• A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models [96.64934195520802]
  We introduce CrashTwin, a physics-grounded evaluation framework designed to stress-test the physical trustworthiness of world models.<n>CrashTwin couples a diverse dataset of multi-agent collision scenarios, comprising 25K controllable synthetic 12K in-the-wild real-world collision sequences.<n>We propose a diagnostic suite that systematically evaluates three dimensions:-temporal consistency, momentum and kinetic energy conservation, and world-dynamics integrity.
  arXiv  Detail & Related papers  (2026-06-27T06:13:35Z)
• Variational Grey-Box Dynamics Matching [45.595103078998385]
  We present a novel grey-box method that integrates incomplete physics models directly into generative models.<n>Our approach learns dynamics from observational trajectories alone, without ground-truth physics parameters.<n>Our experiments on representative ODE/PDE problems show that our method performs on par with or superior to fully data-driven approaches.
  arXiv  Detail & Related papers  (2026-02-19T15:43:22Z)
• Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics [51.85385061275941]
  Molecular dynamics (MD) simulations remain the gold standard for studying protein dynamics.<n>Recent generative models have shown promise in accelerating simulations, yet they struggle with long-horizon generation.<n>We present STAR-MD, a scalable diffusion model that generates physically plausible protein trajectories over micro-scale timescales.
  arXiv  Detail & Related papers  (2026-02-02T14:13:28Z)
• Differentiable Physics-Neural Models enable Learning of Non-Markovian Closures for Accelerated Coarse-Grained Physics Simulations [0.11874952582465602]
  This work presents a hybrid physics-neural model that predicts scalar transport in a complex domain orders of magnitude faster than the 3D simulation.<n>Overall results show that this differentiable physics-neural framework enables fast, accurate, and generalizable coarse-grained surrogates for physical phenomena.
  arXiv  Detail & Related papers  (2025-11-26T13:13:30Z)
• A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems [0.0]
  We present a physics-informed RBC surrogate model for convection.<n>Our approach combines a canonical dimensional neural networks, for spatial reduction, with an innovative recurrent architecture, inspired by large language models.<n>This model replicates key physical features while reducing computational cost, offering a scalable alternative to DNS for long-term simulations.
  arXiv  Detail & Related papers  (2025-05-16T06:47:00Z)
• Trajectory Flow Matching with Applications to Clinical Time Series Modeling [77.58277281319253]
  Trajectory Flow Matching (TFM) trains a Neural SDE in a simulation-free manner, bypassing backpropagation through the dynamics.<n>We demonstrate improved performance on three clinical time series datasets in terms of absolute performance and uncertainty prediction.
  arXiv  Detail & Related papers  (2024-10-28T15:54:50Z)
• Parametric model reduction of mean-field and stochastic systems via higher-order action matching [1.1509084774278489]
  We learn models of population dynamics of physical systems that feature gradient and mean-field effects. We show that our approach accurately predicts population dynamics over a wide range of parameters and outperforms state-of-the-art diffusion-based and flow-based modeling.
  arXiv  Detail & Related papers  (2024-10-15T19:05:28Z)
• Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [55.13352174687475]
  This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which generalizes weather forecasts to finer-grained temporal scales beyond training dataset.<n>Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale.<n>We also introduce a lead time-aware training framework to promote the generalization of the model at different lead times.
  arXiv  Detail & Related papers  (2024-05-22T16:21:02Z)
• Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution [0.0]
  Current AI-based weather models can only provide short-term forecasts accurately when time-integrated beyond a few weeks or a few months.<n>The cause of the instabilities is unknown, and the methods that are used to improve their stability horizons are ad-hoc and lack rigorous theory.<n>We develop long-term physically-consistent data-driven models for the climate system and demonstrate accurate short-term forecasts.
  arXiv  Detail & Related papers  (2023-04-14T09:49:11Z)
• Learning to Simulate Complex Physics with Graph Networks [68.43901833812448]
  We present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains. Our framework---which we term "Graph Network-based Simulators" (GNS)--represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing. Our results show that our model can generalize from single-timestep predictions with thousands of particles during training, to different initial conditions, thousands of timesteps, and at least an order of magnitude more particles at test time.
  arXiv  Detail & Related papers  (2020-02-21T16:44:28Z)

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.