Investigating Inductive Biases for Machine Learning Emulation of Sudden Stratospheric Warmings in Idealised Isca Simulations

• URL: http://arxiv.org/abs/2606.18857v1
• Date: Wed, 17 Jun 2026 09:40:00 GMT
• Title: Investigating Inductive Biases for Machine Learning Emulation of Sudden Stratospheric Warmings in Idealised Isca Simulations
• Abstract summary: A key challenge is whether the models can exploit predictability anchors, such as stratospheric variability, that influence tropospheric circulation beyond short lead times.<n>We test how architectural inductive bias affects emulation of sudden stratospheric warming (SSW) dynamics.<n>Our results identify explicit three-dimensional vertical coupling as a key inductive bias for machine-learning emulation of stratospheric dynamics.
• Score: 1.690922615975256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine-learning emulators are increasingly used for weather prediction and have the potential to extend skill on subseasonal-to-seasonal timescales by learning dynamically important sources of predictability. A key challenge is whether the models can exploit predictability anchors, such as stratospheric variability, that influence tropospheric circulation beyond short lead times. We test how architectural inductive bias affects emulation of sudden stratospheric warming (SSW) dynamics using paired idealised Isca simulations that differ only in an imposed wave-2 heating perturbation. Across convolutional, transformer, and graph-based architectures trained for one-step prediction, model differences are modest when the stratosphere is dynamically quiet but widen substantially when SSW-like variability is active. Our results identify explicit three-dimensional vertical coupling as a key inductive bias for machine-learning emulation of stratospheric dynamics. However, Eliassen-Palm flux diagnostics show that low forecast error does not guarantee physically faithful wave-mean-flow interaction, with coherent errors remaining in stratospheric wave-driving structure.

Related papers

• Climate Physics Dynamic Matching [46.03496190842378]
  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.
  arXiv  Detail & Related papers  (2026-08-27T10:07:40Z)
• Virtual Temperature Sensors in Power Transformers Using Neural Ordinary Differential Equations [42.50242605797505]
  This paper develops a physics-aware Neural Ordinary Differential Equation framework for forecasting transformer thermal behavior.<n>A key contribution is the integration of simplified heat-transfer equations directly into the Neural ODE formulation.<n>The framework provides a standardized, physics-aware, and robust forecasting approach for heterogeneous transformer units.
  arXiv  Detail & Related papers  (2026-08-13T13:59:32Z)
• Physics-Informed Machine Learning for Short-Term Flood Prediction [0.3186130813218338]
  We propose a framework that incorporates hydrological knowledge directly into the loss function of an LSTM model.<n>Specifically, a Trend Alignment constraint penalizes directional inconsistencies between precipitation and discharge trends.<n>This regularization encourages the model to learn physically plausible hydrograph behavior, even with limited training data.
  arXiv  Detail & Related papers  (2026-06-02T19:01:06Z)
• PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting [62.22445401483844]
  Existing methods are severely constrained by the bottleneck of compounding errors.<n>We propose a universal framework called -Cast-Corrector (Plug-and-Play Corrector)<n>Our method significantly enhances the long-term stability of coupled forecasting systems.
  arXiv  Detail & Related papers  (2026-05-09T13:12:33Z)
• Hierarchical Testing of a Hybrid Machine Learning-Physics Global Atmosphere Model [9.985969370583426]
  Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions.<n>Here, we design three sets of experiments targeting synoptic-scale phenomena, interannual variability, and out-of-distribution uniform-warming forcings.<n>We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML-based component, against observations and physics-based Earth system models (ESMs).
  arXiv  Detail & Related papers  (2026-02-11T19:34:50Z)
• PhysE-Inv: A Physics-Encoded Inverse Modeling approach for Arctic Snow Depth Prediction [0.16206783799607727]
  We introduce PhysE-Inv, a novel framework that integrates a sophisticated sequential architecture, an LSTM-Decoder with Multi-head Attention and physics-guided contrastive learning, with physics-guided inference.<n> PhysE-Inv significantly improves prediction performance, reducing error by 20% while demonstrating superior physical consistency and resilience to data sparsity compared to empirical methods.<n>This approach pioneers a path for noise-tolerant, interpretable inverse modeling, with wide applicability in geospatial and cryospheric domains.
  arXiv  Detail & Related papers  (2026-01-23T00:43:51Z)
• Real-time distortion prediction in metallic additive manufacturing via a physics-informed neural operator approach [3.607834195988809]
  This paper proposes a physics-informed smart Neuralhorizon Operator (PINO) to predict z and y-direction for the future 15 s.<n>The performance of PINO model highlights its potential for real-time long-time distortion field prediction in controlling defects.
  arXiv  Detail & Related papers  (2025-11-17T09:37:04Z)
• Forecasting Seismic Waveforms: A Deep Learning Approach for Einstein Telescope [46.4942794596355]
  We introduce textitSeismoGPT, a transformer-based model for forecasting three-component seismic waveforms in the context of future gravitational wave detectors like the Einstein Telescope.<n>By learning temporal and spatial dependencies directly from waveform data, SeismoGPT captures realistic ground motion patterns and provides accurate short-term forecasts.
  arXiv  Detail & Related papers  (2025-09-25T19:16:13Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• 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)
• Probing eigenstate thermalization in quantum simulators via fluctuation-dissipation relations [77.34726150561087]
  The eigenstate thermalization hypothesis (ETH) offers a universal mechanism for the approach to equilibrium of closed quantum many-body systems. Here, we propose a theory-independent route to probe the full ETH in quantum simulators by observing the emergence of fluctuation-dissipation relations. Our work presents a theory-independent way to characterize thermalization in quantum simulators and paves the way to quantum simulate condensed matter pump-probe experiments.
  arXiv  Detail & Related papers  (2020-07-20T18:00:02Z)

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.