arxiv_ml 85% Match Research Paper Planetary Scientists,Astrophysicists,Climate Modelers,Machine Learning Researchers,Computational Scientists 4 days ago

Accelerating Radiative Transfer for Planetary Atmospheres by Orders of Magnitude with a Transformer-Based Machine Learning Model

generative-ai › autoregressive

📄 Abstract

Abstract: Radiative transfer calculations are essential for modeling planetary atmospheres. However, standard methods are computationally demanding and impose accuracy-speed trade-offs. High computational costs force numerical simplifications in large models (e.g., General Circulation Models) that degrade the accuracy of the simulation. Radiative transfer calculations are an ideal candidate for machine learning emulation: fundamentally, it is a well-defined physical mapping from a static atmospheric profile to the resulting fluxes, and high-fidelity training data can be created from first principles calculations. We developed a radiative transfer emulator using an encoder-only transformer neural network architecture, trained on 1D profiles representative of solar-composition hot Jupiter atmospheres. Our emulator reproduced bolometric two-stream layer fluxes with mean test set errors of ~1% compared to the traditional method and achieved speedups of 100x. Emulating radiative transfer with machine learning opens up the possibility for faster and more accurate routines within planetary atmospheric models such as GCMs.

Authors (4)

Isaac Malsky

Tiffany Kataria

Natasha E. Batalha

Matthew Graham

Submitted

October 30, 2025

arXiv Category

astro-ph.EP

arXiv PDF

Key Contributions

Develops a transformer-based machine learning model that emulates radiative transfer calculations for planetary atmospheres, achieving speedups of 100x with mean test set errors of ~1% compared to traditional methods. This significantly reduces computational costs, enabling more accurate and detailed simulations in large-scale models like General Circulation Models.

Business Value

Enables faster and more accurate climate and atmospheric simulations for planets, leading to better understanding of exoplanet atmospheres and potentially improving Earth's climate models. This can accelerate scientific discovery and inform climate policy.

Paper Metadata

Innovation Type

Algorithmic Improvement / Model Application

Deployment Feasibility

High, as it's an emulator that can be integrated into existing simulation pipelines.

Limitations Addressed

Computational demands of standard radiative transfer methods,Accuracy-speed trade-offs in atmospheric modeling,Degradation of simulation accuracy due to simplifications

Performance Gains

100x speedup in radiative transfer calculations,Reduced error (~1%) in flux calculations compared to traditional methods

Technical Tags

radiative transferplanetary atmospherestransformermachine learning emulatorcomputational costspeedupencoder-only transformerflux calculation

Research Topics

Planetary Atmosphere ModelingRadiative TransferMachine Learning EmulationTransformer NetworksComputational Astrophysics

Methods & Architectures

Encoder-only Transformer neural networkMachine Learning EmulationFirst principles calculations (for training data) Transformer (encoder-only)

Applications & Tasks

Planetary Science Astrophysics Climate Modeling High computational cost of radiative transfer calculationsAccuracy-speed trade-offs in atmospheric modelsDegradation of accuracy due to numerical simplifications Accelerating radiative transfer calculationsEmulating radiative transfer for planetary atmosphere modeling

Datasets & Benchmarks

Benchmarks

Speedup: 100x • Mean test set errors: ~1% (for bolometric two-stream layer fluxes)

Accuracy of flux calculationSpeedup factorMean Squared Error (implied)

Related Fields

Machine LearningAstrophysicsPlanetary ScienceClimate ScienceDeep LearningComputational Science

Keywords

Radiative TransferPlanetary AtmospheresTransformerMachine LearningEmulatorAstrophysicsClimate ModelingComputational EfficiencyDeep LearningExoplanets

Academic Context

#Planetary Atmosphere Modeling#Radiative Transfer#Machine Learning Emulation#Transformer Networks#Computational Astrophysics

Commercial Potential

Potential Products

Accelerated radiative transfer simulation softwareAI-powered tools for climate and atmospheric modeling

Target Industries

AerospaceScientific ResearchEnvironmental MonitoringClimate Technology

Use Case Examples

Simulating the atmospheres of exoplanetsImproving accuracy of Earth's climate modelsAnalyzing atmospheric composition of planets in our solar system

Competitive Edge

Offers a significant computational advantage over traditional radiative transfer methods while maintaining high accuracy, making complex atmospheric simulations more feasible.

Market Opportunity

Significant market within scientific simulation and climate modeling software.

Revenue Models

Licensing of the emulator technologydevelopment of specialized simulation software.

Resource Requirements

Compute Needs

High for training the transformer model; significantly reduced for inference compared to traditional methods.

Data Requirements

Requires high-fidelity training data generated from first principles calculations of radiative transfer.

Deployment Constraints

Integration into existing simulation frameworks might require adaptation.

Scalability

The transformer architecture is generally scalable, and the emulator significantly speeds up calculations.

Regulatory Considerations

None.

Production Readiness

Maturity Level

Research/Development

Time to Market

1-3 years, for integration into operational climate models.

Patent Potential

Moderate, for the novel application of transformers to radiative transfer emulation.

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