ChaosNexus: A Foundation Model for Universal Chaotic System Forecasting with Multi-scale Representations

• URL: http://arxiv.org/abs/2509.21802v1
• Date: Fri, 26 Sep 2025 02:59:12 GMT
• Title: ChaosNexus: A Foundation Model for Universal Chaotic System Forecasting with Multi-scale Representations
• Authors: Chang Liu, Bohao Zhao, Jingtao Ding, Yong Li,
• Abstract summary: ChaosNexus is a foundation model pre-trained on a diverse corpus of chaotic dynamics.<n>It demonstrates state-of-the-art zero-shot generalization across both synthetic and real-world benchmarks.
• Score: 15.381819123860259
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Accurately forecasting chaotic systems, prevalent in domains such as weather prediction and fluid dynamics, remains a significant scientific challenge. The inherent sensitivity of these systems to initial conditions, coupled with a scarcity of observational data, severely constrains traditional modeling approaches. Since these models are typically trained for a specific system, they lack the generalization capacity necessary for real-world applications, which demand robust zero-shot or few-shot forecasting on novel or data-limited scenarios. To overcome this generalization barrier, we propose ChaosNexus, a foundation model pre-trained on a diverse corpus of chaotic dynamics. ChaosNexus employs a novel multi-scale architecture named ScaleFormer augmented with Mixture-of-Experts layers, to capture both universal patterns and system-specific behaviors. The model demonstrates state-of-the-art zero-shot generalization across both synthetic and real-world benchmarks. On a large-scale testbed comprising over 9,000 synthetic chaotic systems, it improves the fidelity of long-term attractor statistics by more than 40% compared to the leading baseline. This robust performance extends to real-world applications with exceptional data efficiency. For instance, in 5-day global weather forecasting, ChaosNexus achieves a competitive zero-shot mean error below 1 degree, a result that further improves with few-shot fine-tuning. Moreover, experiments on the scaling behavior of ChaosNexus provide a guiding principle for scientific foundation models: cross-system generalization stems from the diversity of training systems, rather than sheer data volume.

Related papers

• Hierarchical Inference and Closure Learning via Adaptive Surrogates for ODEs and PDEs [15.38864225184245]
  Inverse problems are the task of calibrating models to match data.<n>We develop a principled methodology for leveraging data from collections of distinct yet related physical systems.<n>We learn the shared unknown dynamics in the form of an ML-based closure model.
  arXiv  Detail & Related papers  (2026-03-04T10:30:08Z)
• Out-of-Support Generalisation via Weight Space Sequence Modelling [1.6921396880325779]
  We reformulate the OoS generalisation problem as a sequence modelling task in the weight space.<n>Our framework yields plausible, interpretable, and uncertainty-aware predictions without necessitating explicit inductive biases.<n>These results hold significant implications for the wider adoption of artificial intelligence in safety-critical applications.
  arXiv  Detail & Related papers  (2026-02-14T01:51:54Z)
• Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting [63.8116386935854]
  We demonstrate that state-of-the-art probabilistic skill requires neither intricate architectural constraints nor specialized trainings.<n>We introduce a scalable framework for learning multi-scale atmospheric dynamics by combining a directly downsampled latent space with a history-conditioned local projector.<n>We find that our framework design is robust to the choice of probabilistic estimators, seamlessly supporting interpolants, diffusion models, and CRPS-based ensemble training.
  arXiv  Detail & Related papers  (2026-01-26T03:52:16Z)
• SynCast: Synergizing Contradictions in Precipitation Nowcasting via Diffusion Sequential Preference Optimization [62.958457694151384]
  We introduce preference optimization into precipitation nowcasting for the first time, motivated by the success of reinforcement learning from human feedback in large language models.<n>In the first stage, the framework focuses on reducing FAR, training the model to effectively suppress false alarms.
  arXiv  Detail & Related papers  (2025-10-22T16:11:22Z)
• Learning to Predict Chaos: Curriculum-Driven Training for Robust Forecasting of Chaotic Dynamics [0.0]
  CCF organizes training data based on fundamental principles of dynamical systems theory.<n>CCF enables the model to build a robust and generalizable representation of dynamical behaviors.<n>CCF extends the valid prediction horizon by up to 40% compared to random-order training.
  arXiv  Detail & Related papers  (2025-10-05T20:06:16Z)
• Towards Efficient General Feature Prediction in Masked Skeleton Modeling [59.46799426434277]
  We propose a novel General Feature Prediction framework (GFP) for efficient mask skeleton modeling.<n>Our key innovation is replacing conventional low-level reconstruction with high-level feature prediction that spans from local motion patterns to global semantic representations.
  arXiv  Detail & Related papers  (2025-09-03T18:05:02Z)
• Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks [59.552873049024775]
  We show that compute-optimally trained models exhibit a remarkably precise universality.<n>With learning rate decay, the collapse becomes so tight that differences in the normalized curves across models fall below the noise floor.<n>We explain these phenomena by connecting collapse to the power-law structure in typical neural scaling laws.
  arXiv  Detail & Related papers  (2025-07-02T20:03:34Z)
• Consistent World Models via Foresight Diffusion [56.45012929930605]
  We argue that a key bottleneck in learning consistent diffusion-based world models lies in the suboptimal predictive ability.<n>We propose Foresight Diffusion (ForeDiff), a diffusion-based world modeling framework that enhances consistency by decoupling condition understanding from target denoising.
  arXiv  Detail & Related papers  (2025-05-22T10:01:59Z)
• Hierarchically Disentangled Recurrent Network for Factorizing System Dynamics of Multi-scale Systems: An application on Hydrological Systems [4.634606500665259]
  We propose a novel hierarchical recurrent neural architecture that factorizes the system dynamics at multiple temporal scales.<n> Experiments on several catchments from the National Weather Service North Central River Forecast Center show that FHNN outperforms standard baselines.<n>We show that FHNN can maintain accuracy even with limited training data through effective pre-training strategies and training global models.
  arXiv  Detail & Related papers  (2024-07-29T16:25:43Z)
• Zero-shot Safety Prediction for Autonomous Robots with Foundation World Models [0.12499537119440243]
  A world model creates a surrogate world to train a controller and predict safety violations by learning the internal dynamic model of systems. We propose foundation world models that embed observations into meaningful and causally latent representations. This enables the surrogate dynamics to directly predict causal future states by leveraging a training-free large language model.
  arXiv  Detail & Related papers  (2024-03-30T20:03:49Z)
• eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling [9.52474299688276]
  We introduce a low-rank structured variational autoencoder framework for nonlinear state-space graphical models. We show that our approach consistently demonstrates the ability to learn a more predictive generative model.
  arXiv  Detail & Related papers  (2024-03-03T02:19:49Z)
• Learning Robust Precipitation Forecaster by Temporal Frame Interpolation [65.5045412005064]
  We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies. Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
  arXiv  Detail & Related papers  (2023-11-30T08:22:08Z)
• Model scale versus domain knowledge in statistical forecasting of chaotic systems [7.6146285961466]
  We benchmark 24 state-of-the-art forecasting methods on a crowdsourced database of 135 low-dimensional systems with 17 forecast metrics. We find that large-scale, domain-agnostic forecasting methods consistently produce predictions that remain accurate up to two dozen Lyapunov times. In data-limited settings outside the long-horizon regime, we find that physics-based hybrid methods retain a comparative advantage due to their strong inductive biases.
  arXiv  Detail & Related papers  (2023-03-13T03:03:17Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)

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.